Дисертації з теми "Multitemporel"
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1
Alvarez, padilla Francisco Javier. "AIMM - Analyse d'Images nucléaires dans un contexte Multimodal et Multitemporel." Thesis, Reims, 2019. http://www.theses.fr/2019REIMS017/document.
Анотація:
Ces travaux de thèse portent sur la proposition de stratégies de segmentation des tumeurs cancéreuses dans un contexte multimodal et multitemporel. La multimodalité fait référence au couplage de données TEP/TDM pour exploiter conjointement les deux sources d’information pour améliorer les performances de la segmentation. La multitemporalité fait référence à la disposition des images acquises à différents dates, ce qui limite une correspondance spatiale possible entre elles.Dans une première méthode, une structure arborescente est utilisée pour traiter et pour extraire des informations afin d’alimenter une segmentation par marche aléatoire. Un ensemble d'attributs est utilisé pour caractériser les nœuds de l'arbre, puis le filtrer et projeter des informations afin de créer une image vectorielle. Un marcheur aléatoire guidé par les données vectorielles provenant de l'arbre est utilisé pour étiqueter les voxels à des fins de segmentation.La deuxième méthode traite le problème de la multitemporalité en modifiant le paradigme de voxel à voxel par celui de nœud à nœud. Deux arbres sont alors modélisés à partir de la TEP et de la TDM avec injection de contraste pour comparer leurs nœuds par une différence entre leurs attributs et ainsi correspondre à ceux considérés comme similaires en supprimant ceux qui ne le sont pas.Dans une troisième méthode, qui est une extension de la première, l'arbre calculé à partir de l'image est directement utilisé pour mettre en œuvre l'algorithme développé. Une structure arborescente est construite sur la TEP, puis les données TDM sont projetées sur l’arbre en tant qu’informations contextuelles. Un algorithme de stabilité de nœud est appliqué afin de détecter et d'élaguer les nœuds instables. Des graines, extraites de la TEP, sont projetées dans l'arbre pour fournir des étiquettes (pour la tumeur et le fond) à ses nœuds correspondants et les propager au sein de la hiérarchie. Les régions évaluées comme incertaines sont soumises à une méthode de marche aléatoire vectorielle pour compléter l'étiquetage de l'arbre et finaliser la segmentationThis work focuses on the proposition of cancerous tumor segmentation strategies in a multimodal and multitemporal context. Multimodal scope refers to coupling PET/CT data in order to jointly exploit both information sources with the purpose of improving segmentation performance. Multitemporal scope refers to the use of images acquired at different dates, which limits a possible spatial correspondence between them.In a first method, a tree is used to process and extract information dedicated to feed a random walker segmentation. A set of region-based attributes is used to characterize tree nodes, filter the tree and then project data into the image space for building a vectorial image. A random walker guided by vectorial tree data on image lattice is used to label voxels for segmentation.The second method is geared toward multitemporality problem by changing voxel-to-voxel for node-to-node paradigm. A tree structure is thus applied to model two hierarchical graphs from PET and contrast-enhanced CT, respectively, and compare attribute distances between their nodes to match those assumed similar whereas discarding the others.In a third method, namely an extension of the first one, the tree is directly involved as the data-structure for algorithm application. A tree structure is built on the PET image, and CT data is then projected onto the tree as contextual information. A node stability algorithm is applied to detect and prune unstable attribute nodes. PET-based seeds are projected into the tree to assign node seed labels (tumor and background) and propagate them by hierarchy. The uncertain nodes, with region-based attributes as descriptors, are involved in a vectorial random walker method to complete tree labeling and build the segmentation
2
BAPPEL, Eric Albert. "Apport de la teledetection aerospatiale pour l'a ide à la gestion de la sole canniere reunionnaise." Phd thesis, Université de la Réunion, 2005. http://tel.archives-ouvertes.fr/tel-00489730.
Анотація:
L'objectif de cette thèse est d'étudier les potentialités de la télédétection aérospatiale pour l'aide à la gestion de sole cannière Réunionnaise. Nous avons utilisé une base de données d'images multitemporelles SPOT 4&5 (années 2002 et 2003) et organisé une campagne d'acquisition d'images hyperspectrales CASI en septembre 2002. Simultanément, nous avons assuré le déroulement et la mise en place d'un protocole de mesures au champ pour suivre l'évolution des paramètres biophysiques descriptifs de l'état du couvert de la canne (surface foliaire, taux d'azote, biomasse de la culture) et des paramètres agronomiques (suivi des coupes et des replantations). Les résultats ont montré qu'il est possible d'estimer la surface foliaire (LAI) à partir de l'indice de végétation normalisé (NDVI) ainsi que le rendement canne à partir de l'indice de végétation NDVI calculé au moment du développement maximal du couvert. Avec les données SPOT, la meilleure estimation du rendement canne à l'échelle parcellaire résulte du couplage entre le modèle de croissance Mosicas et les profils d'évolution de surface foliaire obtenus à partir des images SPOT 4&5. Les données hyperspectrales CASI permettent une meilleure estimation de la surface foliaire et de la biomasse fraîche que les données SPOT 4&5 ainsi qu'une estimation du taux d'azote foliaire qui est, en phase de maturation, un indicateur de richesse en sucre. La possibilité de discriminer des parcelles de canne en fonction de leurs états de surface (pleine végétation, coupée ou labourée) nous a permis de développer des applications opérationnelles de cartographie dynamique de la sole cannière en temps quasi réel : le suivi des coupes et des replantations.
3
Gimenez, Rollin. "Exploitation de données optiques multimodales pour la cartographie des espèces végétales suivant leur sensibilité aux impacts anthropiques." Electronic Thesis or Diss., Toulouse, ISAE, 2023. http://www.theses.fr/2023ESAE0030.
Анотація:
Les impacts anthropiques sur les sols végétalisés sont difficiles à caractériser à l'aide d’instruments de télédétection optique. Ces impacts peuvent cependant entrainer de graves conséquences environnementales. Leur détection indirecte est rendue possible par les altérations provoquées sur la biocénose et la physiologie des plantes, qui se traduisent par des changements de propriétés optiques au niveau de la plante et de la canopée. L'objectif de cette thèse est de cartographier les espèces végétales en fonction de leur sensibilité aux impacts anthropiques à l'aide de données de télédétection optique multimodale. Différents impacts anthropiques associés à des activités industrielles passées sont considérés (présence d'hydrocarbures dans le sol, contamination chimique polymétallique, remaniement et compactage du sol, etc.) dans un contexte végétal complexe (distribution hétérogène de diverses espèces de différentes strates). Les informations spectrales, temporelles et/ou morphologiques sont utilisées pour identifier les genres et espèces et caractériser leur état de santé afin de définir et de cartographier leur sensibilité aux différents impacts anthropiques. Des images hyperspectrales aéroportées, des séries temporelles Sentinel-2 et des modèles numériques d'élévation sont exploités indépendamment ou combinés. La démarche proposée repose sur trois étapes. La première consiste à cartographier les impacts anthropiques en combinant des données de télédétection optique et des données fournies par l'opérateur du site (analyses de sol, cartes d'activité, etc.). La seconde étape vise à développer une méthode de cartographie de la végétation à l'aide de données de télédétection optique adaptée à des contextes complexes tels que les sites industriels. Enfin, les variations de la biodiversité et des traits fonctionnels dérivées des images hyperspectrales aéroportées et des modèles numériques d'élévation sont analysées en relation avec la carte d'impact au cours de la troisième étape. Les espèces identifiées comme espèces invasives ainsi que celles en lien avec les pratiques agricoles et forestières et les mesures de biodiversité renseignent sur les impacts biologiques. La cartographie des strates de végétation et la caractérisation de la hauteur des arbres, liées à une succession secondaire, sont utilisées pour détecter les impacts physiques (remaniement du sol, excavations). Enfin, les conséquences du stress induit sur la signature spectrale des espèces sensibles permettent d'identifier les impacts chimiques. Plus précisément, dans le contexte de l'étude, les signatures spectrales de Quercus spp, Alnus glutinosa et des mélanges herbacés varient en fonction de l'acidité du sol, tandis que celles de Platanus x hispanica et des mélanges arbustifs présentent des différences dues aux autres impacts chimiquesAnthropogenic impacts on vegetated soils are difficult to characterize using optical remote sensing devices. However, these impacts can lead to serious environmental consequences. Their indirect detection is made possible by the induced alterations to biocenosis and plant physiology, which result in optical property changes at plant and canopy levels. The objective of this thesis is to map plant species based on their sensitivity to anthropogenic impacts using multimodal optical remote sensing data. Various anthropogenic impacts associated with past industrial activities are considered (presence of hydrocarbons in the soil, polymetallic chemical contamination, soil reworking and compaction, etc.) in a complex plant context (heterogeneous distribution of multiple species from different strata). Spectral, temporal and/or morphological information is used to identify genera and species and characterise their health status to define and map their sensitivity to the various anthropogenic impacts. Hyperspectral airborne images, Sentinel-2 time series and digital elevation models are then used independently or combined. The proposed scientific approach consists of three stages. The first one involves mapping anthropogenic impacts at site level by combining optical remote sensing data with data supplied by the site operator (soil analyses, activity maps, etc.). The second stage seeks to develop a vegetation mapping method using optical remote sensing data suitable to complex contexts like industrial sites. Finally, the variations in biodiversity and functional response traits derived from airborne hyperspectral images and digital elevation models are analysed in relation to the impact map during the third stage. The species identified as invasive species, as well as those related to agricultural and forestry practices, and biodiversity measures provide information about biological impacts. Vegetation strata mapping and characterisation of tree height, linked to secondary succession, are used to detect physical impacts (soil reworking, excavations). Finally, the consequences of induced stress on the spectral signature of susceptible species allow the identification of chemical impacts. Specifically, in the study context, the spectral signatures of Quercus spp., Alnus glutinosa, and grass mixtures vary with soil acidity, while those of Platanus x hispanica and shrub mixtures exhibit differences due to other chemical impacts
4
BECCATI, Alan. "Multi-sensor Evolution Analysis: an advanced GIS for interactive time series analysis and modelling based on satellite data." Doctoral thesis, Università degli studi di Ferrara, 2011. http://hdl.handle.net/11392/2388733.
Анотація:
Archives of Earth remote sensing data, acquired from orbiting satellites, contain large amounts of information that can be used both for research activities and decision support. Thematic categorization is one method to extract from satellite data meaningful information that humans can directly comprehend. An interactive system that permits to analyse geo-referenced thematic data and its evolution over time is proposed as a tool to efficiently exploit such vast and growing amount of data. This thesis describes the approach used in building the system, the data processing methodology, details architectural elements and graphical interfaces. Finally, this thesis provides an evaluation of potential uses of the features provided, performance levels and usability of an implementation hosting an archive of 15 years moderate resolution (1 Km, from the ATSR instrument) thematic data.
5
BAPPEL, Eric Albert. "APPORT DE LA TELEDETECTION AEROSPATIALE POUR L'AIDE A LA GESTION DE LA SOLE CANNIERE REUNIONNAISE." Phd thesis, Université de la Réunion, 2005. http://tel.archives-ouvertes.fr/tel-00009403.
Анотація:
L'objectif de cette thèse est d'étudier les potentialités de la télédétection aérospatiale pour l'aide à la gestion de sole cannière Réunionnaise. Nous avons utilisé une base de données d'images multitemporelles SPOT 4&5 (années 2002 et 2003) et organisé une campagne d'acquisition d'images hyperspectrales CASI en septembre 2002. Simultanément, nous avons assuré le déroulement et la mise en place d'un protocole de mesures au champ pour suivre l'évolution des paramètres biophysiques descriptifs de l'état du couvert de la canne (surface foliaire, taux d'azote, biomasse de la culture) et des paramètres agronomiques (suivi des coupes et des replantations). Les résultats ont montré qu'il est possible d'estimer la surface foliaire (LAI) à partir de l'indice de végétation normalisé (NDVI) ainsi que le rendement canne à partir de l'indice de végétation NDVI calculé au moment du développement maximal du couvert. Avec les données SPOT, la meilleure estimation du rendement canne à l'échelle parcellaire résulte du couplage entre le modèle de croissance Mosicas et les profils d'évolution de surface foliaire obtenus à partir des images SPOT 4&5. Les données hyperspectrales CASI permettent une meilleure estimation de la surface foliaire et de la biomasse fraîche que les données SPOT 4&5 ainsi qu'une estimation du taux d'azote foliaire qui est, en phase de maturation, un indicateur de richesse en sucre. La possibilité de discriminer des parcelles de canne en fonction de leurs états de surface (pleine végétation, coupée ou labourée) nous a permis de développer des applications opérationnelles de cartographie dynamique de la sole cannière en temps quasi réel : le suivi des coupes et des replantations.
6
Lê, Thu Trang. "Extraction d'informations de changement à partir des séries temporelles d'images radar à synthèse d'ouverture." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAA020/document.
Анотація:
La réussite du lancement d'un grand nombre des satellites Radar à Synthèse d'Ouverture (RSO - SAR) de nouvelle génération a fourni régulièrement des images SAR et SAR polarimétrique (PolSAR) multitemporelles à haute et très haute résolution spatiale sur de larges régions de la surface de la Terre. Le système SAR est approprié pour des tâches de surveillance continue ou il offre l'avantage d'être indépendant de l'éclairement solaire et de la couverture nuageuse. Avec des données multitemporelles, l'information spatiale et temporelle peut être exploitée simultanément pour rendre plus concise, l'extraction d'information à partir des données. La détection de changement de structures spécifiques dans un certain intervalle de temps nécessite un traitement complexe des données SAR et la présence du chatoiement (speckle) qui affecte la rétrodiffusion comme un bruit multiplicatif. Le but de cette thèse est de fournir une méthodologie pour simplifier l'analyse des données multitemporelles SAR. Cette méthodologie doit bénéficier des avantages d'acquisitions SAR répétitives et être capable de traiter différents types de données SAR (images SAR mono-, multi- composantes, etc.) pour diverses applications. Au cours de cette thèse, nous proposons tout d'abord une méthode générale basée sur une matrice d'information spatio-temporelle appelée Matrice de détection de changement (CDM). Cette matrice contient des informations de changements obtenus à partir de tests croisés de similarité sur des voisinages adaptatifs. La méthode proposée est ensuite exploitée pour réaliser trois tâches différentes: 1) la détection de changement multitemporel avec différents types de changements, ce qui permet la combinaison des cartes de changement entre des paires d'images pour améliorer la performance de résultat de détection de changement; 2) l'analyse de la dynamicité de changement de la zone observée, ce qui permet l'étude de l'évolution temporelle des objets d'intérêt; 3) le filtrage nonlocal temporel des séries temporelles d'images SAR/PolSAR, ce qui permet d'éviter le lissage des informations de changement dans des séries pendant le processus de filtrage.Afin d'illustrer la pertinence de la méthode proposée, la partie expérimentale de la thèse est effectuée sur deux sites d'étude: Chamonix Mont-Blanc, France et le volcan Merapi, Indonésie, avec différents types de changements (i.e. évolution saisonnière, glaciers, éruption volcanique, etc.). Les observations de ces sites d'étude sont acquises sur quatre séries temporelles d'images SAR monocomposantes et multicomposantes de moyenne à haute et très haute résolution: des séries temporelles d'images Sentinel-1, ALOS-PALSAR, RADARSAT-2 et TerraSAR-XA large number of successfully launched and operated Synthetic Aperture Radar (SAR) satellites has regularly provided multitemporal SAR and polarimetric SAR (PolSAR) images with high and very high spatial resolution over immense areas of the Earth surface. SAR system is appropriate for monitoring tasks thanks to the advantage of operating in all-time and all-weather conditions. With multitemporal data, both spatial and temporal information can simultaneously be exploited to improve the results of researche works. Change detection of specific features within a certain time interval has to deal with a complex processing of SAR data and the so-called speckle which affects the backscattered signal as multiplicative noise.The aim of this thesis is to provide a methodology for simplifying the analysis of multitemporal SAR data. Such methodology can benefit from the advantages of repetitive SAR acquisitions and be able to process different kinds of SAR data (i.e. single, multipolarization SAR, etc.) for various applications. In this thesis, we first propose a general framework based on a spatio-temporal information matrix called emph{Change Detection Matrix} (CDM). This matrix contains temporal neighborhoods which are adaptive to changed and unchanged areas thanks to similarity cross tests. Then, the proposed method is used to perform three different tasks:1) multitemporal change detection with different kinds of changes, which allows the combination of multitemporal pair-wise change maps to improve the performance of change detection result;2) analysis of change dynamics in the observed area, which allows the investigation of temporal evolution of objects of interest;3) nonlocal temporal mean filtering of SAR/PolSAR image time series, which allows us to avoid smoothing change information in the time series during the filtering process.In order to illustrate the relevancy of the proposed method, the experimental works of the thesis is performed on four datasets over two test-sites: Chamonix Mont-Blanc, France and Merapi volcano, Indonesia, with different types of changes (i.e., seasonal evolution, glaciers, volcanic eruption, etc.). Observations of these test-sites are performed on four SAR images time series from single polarization to full polarization, from medium to high, very high spatial resolution: Sentinel-1, ALOS-PALSAR, RADARSAT-2 and TerraSAR-X time series
7
Lima, Elaine de Cacia de. "Qualidade multitemporal da paisagem." reponame:Repositório Institucional da UFPR, 2013. http://hdl.handle.net/1884/26113.
Анотація:
O uso inadequado dos recursos naturais foi uma constante no passado em toda a Região Sul do Brasil. Para orientar o uso sustentável desses recursos se faz necessário o desenvolvimento de pesquisas que permitam diagnosticar a qualidade da paisagem de determinado espaço geográfico e que proporcionem soluções adequadas de uso e conservação. Com este propósito foi desenvolvida uma pesquisa em General Carneiro, Estado do Paraná, no Bioma da Floresta Ombrófila Mista, em uma propriedade das Indústrias Pizzatto. Com o objetivo de realizar análise e interpretação multitemporal da qualidade visual da paisagem, utilizando geotecnologias como o sensoriamento remoto e o geoprocessamento. O estudo foi realizado ao longo de uma série temporal de 48 anos, utilizando fotografias aéreas e imagem de satélite IKONOS II, que permitiram obter produtos cartográficos temáticos de Uso e Cobertura do Solo que foram integrados em um SIG no programa SPRING 3.6. Foram inter-relacionados os dados espaciais (polígonos) aos dados não-espaciais (alfanuméricos), onde as informações alfanuméricas correspondem às características das classes interpretadas, como área, perímetro, textura, forma, convergências e divergências e características ambientais e paisagísticas. Realizou-se também uma análise da dinâmica espaço-temporal da fragmentação das classes tipológicas de florestas em estágio (inicial, intermediário e avançado), constatando que esta fragmentação refere-se aos objetivos da propriedade ao longo da série temporal. Foram elaborados também os produtos de potencial erosivo do solo, de conflito de uso e qualidade da paisagem, através do cruzamento entre os mapas utilizando a programação LEGAL (linguagem espacial para geoprocessamento algébrico) no SPRING, através da simultaneidade entre os mapas. Com estes produtos, obteve-se as informações de potencialidade erosiva do solo, com o tema baixo moderado apresentando a maior porcentagem (50%); conflito de uso do solo, com o tema uso adequado sem restrições com a maior porcentagem (> 80%) e qualidade da paisagem de 1952-1980 e 1980-2000, ambos apresentando a classe de baixo grau de antropização acima de 70%. Com a análise dos resultados obtidos, concluiu-se que o recorte espacial apresenta-se com uma alta qualidade paisagística, referente às disponibilidades dos recursos naturais e as forma de utilização do espaço. Constatou-se também a efetividade e aplicabilidade do procedimento metodológico para avaliação da qualidade da paisagem como instrumento para planejamento e gestão de propriedades florestais.
8
Qi, Jiaguo. "Compositing multitemporal remote sensing data." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186327.
Анотація:
In order to reduce the problems of clouds, atmospheric variations, view angle effects, and the soil background variations in the high temporal frequency AVHRR data, a compositing technique is usually employed. Current compositing techniques use a single pixel selection criterion of outputting the input pixel of maximum value NDVI. Problems, however, exist due to the use of the NDVI classifier and to the imperfection of the pixel selection criteria of the algorithm itself. The NDVI was found not to have the maximum value under an ideal observation condition, while the single pixel selection criterion favors the large off-nadir sensor view angles. Consequently, the composited data still consist of substantial noise. To further reduce the noise, several data sets were obtained to study these external factor effects on the NDVI classifier and other vegetation indices. On the basis of the studies of these external factors, a new classifier was developed to further reduce the soil noise. Then, a new set of pixel selection criteria was proposed for compositing. The new compositing algorithm with the new classifier was used to composite two AVHRR data sets. The alternative approach showed that the high frequency noises were greatly reduced, while more valuable data were retained. The proposed alternative compositing algorithm not only further reduced the external factor related noises, but also retained more valuable data. In this dissertation, studies of external factor effects on remote sensing data and derived vegetation indices are presented in the first four chapters. Then the development of the new classifier and the alternative compositing algorithm were described. Perspectives and limitations of the proposed algorithms are also discussed.
9
Yousif, Osama. "Change Detection Using Multitemporal SAR Images." Licentiate thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123494.
Анотація:
Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm.QC 20130610
10
Vicente-Guijalba, Fernando. "Teledetección Multitemporal mediante Dinámica de Sistemas." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/57626.
11
Fröjse, Linda. "Multitemporal Satellite Images for Urban Change Detection." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-38539.
Анотація:
The objective of this research is to detect change in urban areas using two satellite images (from 2001 and 2010) covering the city of Shanghai, China. These satellite images were acquired by Landsat-7 and HJ-1B, two satellites with different sensors. Two change detection algorithms were tested: image differencing and post-classification comparison. For image differencing the difference image was classified using unsupervised k-means classification, the classes were then aggregated into change and no change by visual inspection. For post-classification comparison the images were classified using supervised maximum likelihood classification and then the difference image of the two classifications were classified into change and no change also by visual inspection. Image differencing produced result with poor overall accuracy (band 2: 24.07%, band 3: 25.96%, band 4: 46.93%), while post-classification comparison produced result with better overall accuracy (90.96%). Post-classification comparison works well with images from different sensors, but it relies heavily on the accuracy of the classification. The major downside of the methodology of both algorithms was the large amount of visual inspection.
12
Yousif, Osama. "Urban Change Detection Using Multitemporal SAR Images." Doctoral thesis, KTH, Geoinformatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168216.
Анотація:
Multitemporal SAR images have been increasingly used for the detection of different types of environmental changes. The detection of urban changes using SAR images is complicated due to the complex mixture of the urban environment and the special characteristics of SAR images, for example, the existence of speckle. This thesis investigates urban change detection using multitemporal SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate effective methods for reduction of the speckle effect in change detection, (3) to investigate spatio-contextual change detection, (4) to investigate object-based unsupervised change detection, and (5) to investigate a new technique for object-based change image generation. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR and ENVISAT ASAR sensors were used for pixel-based change detection. For the object-based approaches, TerraSAR-X images were used. In Paper I, the unsupervised detection of urban change was investigated using the Kittler-Illingworth algorithm. A modified ratio operator that combines positive and negative changes was used to construct the change image. Four density function models were tested and compared. Among them, the log-normal and Nakagami ratio models achieved the best results. Despite the good performance of the algorithm, the obtained results suffer from the loss of fine geometric detail in general. This was a consequence of the use of local adaptive filters for speckle suppression. Paper II addresses this problem using the nonlocal means (NLM) denoising algorithm for speckle suppression and detail preservation. In this algorithm, denoising was achieved through a moving weighted average. The weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, principle component analysis (PCA) was used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the number of significant PCA components to be retained for weights computation and the required noise variance were proposed. The experimental results showed that the NLM algorithm successfully suppressed speckle effects, while preserving fine geometric detail in the scene. The analysis also indicates that filtering the change image instead of the individual SAR images was effective in terms of the quality of the results and the time needed to carry out the computation. The Markov random field (MRF) change detection algorithm showed limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle. To overcome this problem, Paper III utilizes the NLM theory to define a nonlocal constraint on pixels class-labels. The iterated conditional mode (ICM) scheme for the optimization of the MRF criterion function is extended to include a new step that maximizes the nonlocal probability model. Compared with the traditional MRF algorithm, the experimental results showed that the proposed algorithm was superior in preserving fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map. Paper IV investigates object-based unsupervised change detection using very high resolution TerraSAR-X images over urban areas. Three algorithms, i.e., Kittler-Illingworth, Otsu, and outlier detection, were tested and compared. The multitemporal images were segmented using multidate segmentation strategy. The analysis reveals that the three algorithms achieved similar accuracies. The achieved accuracies were very close to the maximum possible, given the modified ratio image as an input. This maximum, however, was not very high. This was attributed, partially, to the low capacity of the modified ratio image to accentuate the difference between changed and unchanged areas. Consequently, Paper V proposes a new object-based change image generation technique. The strong intensity variations associated with high resolution and speckle effects render object mean intensity unreliable feature. The modified ratio image is, therefore, less efficient in emphasizing the contrast between the classes. An alternative representation of the change data was proposed. To measure the intensity of change at the object in isolation of disturbances caused by strong intensity variations and speckle effects, two techniques based on the Fourier transform and the Wavelet transform of the change signal were developed. Qualitative and quantitative analyses of the result show that improved change detection accuracies can be obtained by classifying the proposed change variables.QC 20150529
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COSTA, MARIA CLARA DE OLIVEIRA. "A FUZZY MODEL FOR MULTITEMPORAL IMAGE CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8953@1.
Анотація:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORO presente trabalho apresenta a modelagem de conhecimento multitemporal para a classificação automática de cobertura do solo para imagens de satélite. O procedimento de classificação agrega os conhecimentos espectral e multitemporal utilizando conjuntos nebulosos e suas pertinências de classe como informação prévia. O método se baseia no conceito de Redes de Markov Nebulosas, um sistema com um conjunto de estados que a cada instante de tempo troca o estado corrente de acordo com possibilidades associadas a cada um. No caso deste trabalho cada estado representa uma classe, e as possibilidades são estimadas automaticamente a partir de dados históricos de uma mesma região geográfica, empregando algoritmos genéticos. A avaliação experimental utilizou um conjunto de imagens Landsat-5 da cidade do Rio de Janeiro, obtidas em cinco datas separadas por aproximadamente quatro anos. Os resultados indicaram que o uso do conhecimento multitemporal, conforme modelado pelo método proposto traz um significante aumento da eficiência de classificação em comparação à classificação puramente espectral, além de flexibilizar o procedimento de classificação no que diz respeito aos dados necessários para o treinamento do modelo.
This work presents a multitemporal knowledge model for automatic classification of remotely sensed images. The model combines multitemporal and spectral knowledge within a fuzzy framework. This method is based on Fuzzy Markov Chains, a system having a set of states that, at each time, change the current state according to the fuzzy possibilities associated to each one. In this work each state represents one class, and the possibilities are automatically estimated based on historical data by using genetic algorithms. The experimental evaluation was carried through for a set of Landsat-5 TM images of the Rio de Janeiro State, Brazil, acquired at five dates separated by approximately four years. Results indicate that the use of multitemporal knowledge as modeled by the proposed method brings an expressive improvement in efficiency to the classification, when compared to the pure spectral classifier. Besides it, adds flexibility to the classification procedure, concerning to necessary data used for model training.
14
Oighenstein, Anderson Liana. "Multitemporal analysis of evergreen forest dynamics in Amazonia." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534294.
15
Ghannam, Sherin Ghannam. "Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/81092.
Анотація:
The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite.
To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat.
Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes.
In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM.
In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM.Ph. D.
16
Breeden, Charles F. "A multitemporal analysis of Georgia's coastal vegetation, 1990-2005." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-04172008-133241/.
Анотація:
Thesis (M.A.)--Georgia State University, 2008.Title from file title page. Jeremy Diem, committee chair; Jeremy Crampton, John Allensworth, committee members. Electronic text (126 p. : col. maps) : digital, PDF file. Description based on contents viewed July 17, 2008. Includes bibliographical references (p. 110-121).
17
Breeden, Charles III F. "A Multitemporal Analysis of Georgia's Coastal Vegetation, 1990-2005." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/geosciences_theses/10.
Анотація:
Land and vegetation changes are part of the continuous and dynamic cycle of earth system variation. This research examines vegetation changes in the 21-county eco-region along coastal Georgia. The Advanced Very High Resolution Radiometer (AVHRR) with Normalized Difference Vegetation Index (NDVI) data is used in tandem with a Principal Component Analysis (PCA) and climatic variables to determine where, and to what extent vegetation and land cover change is occurring. This research is designed around a 16 year time-series from 1990-2005. Findings were that mean NDVI values were either steady or slightly improved, and that PC1 (Healthiness) and PC2 (Time-Change) explained nearly 99 percent of the total mean variance. Healthiness declines are primarily the result of expanding urban districts and decreased soil moisture while increases are the results of restoration, and increased soil moisture. This research aims to use this analysis for the assessment of land changes as the conduit for future environmental research.
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GUIMARÃES, Ariana Silva. "Análise multitemporal da superfície de manguezal do litoral Norte de Pernambuco : a participação da Aquicultura na conversão de áreas de mangue em viveiro." Universidade Federal Rural de Pernambuco, 2007. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6232.
Анотація:
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Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq
The conversion of mangrove areas to nurseries in Brazil has been reported since the 18th century, mainly in the Northeast region. Due to this history and in addition to the rapid growth of Brazilian shrimp farming, this activity has been blamed for losses of mangrove areas along the Brazilian coast. In order to determine the true participation of aquaculture in this process, a multitemporal study of the mangrove cover of the northern coast of the State of Pernambuco was carried out using remote sensing and geographic information system (GIS) techniques. The time series analyzed were from 1973 to 2005, using satellite images obtained from different sensors (MSS / LANDSAT-1 - 1973; TM / LANDSAT-5 - 1988; TM / LANDSAT-5 - 1999; ETM + / LANSAT-7 - 2001 and CCD / CBERS-2 - 2005). For the mapping of mangroves, only the areas covered by mangrove vegetation were considered, with apicum classified with a distinct class of mangrove class. The study identified that the extension and distribution of the mangrove forest varied considerably during the last three decades in the studied area, with different phases, characterized by the increase and reduction of the mangrove cover. The results obtained were: 1973 = 7,101ha, 1988 = 5,821ha, 1999 = 6,442ha, 2001 = 6,187ha and 2005 = 6,575ha. The areas occupied by nurseries were also evaluated and presented an exponential growth, the latter being more pronounced in recent years, with the following values: 1973 = 0ha, 1988 = 7ha, 1999 = 366ha, 2001 = 1,157ha and 2005 = 1,474ha. The study showed that between 1973 and 2005, there was a reduction of 2,052ha of mangroves, of which 197ha were reduced by conversion to nurseries. In this way, it is possible to affirm that the real contribution of shrimp farming in the conversion of these areas was 9.6% of the total area reduced and that other activities of anthropic origin, such as agriculture, urban expansion and tourism were the main responsible for this scenario Of reduction of mangrove areas in the northern coast of the state of Pernambuco
A conversão de áreas de mangue em viveiros de cultivo no Brasil é relatada desde o século XVIII, principalmente para a região Nordeste. Em função desse histórico e somado ao rápido crescimento da carcinicultura brasileira, esta atividade vem sendo responsabilizada pelas perdas de áreas de mangue ao longo do litoral brasileiro. A fim de determinar a real participação da aqüicultura neste processo, foi realizado um estudo multitemporal da cobertura de manguezal do litoral norte do Estado de Pernambuco, utilizando técnicas de sensoriamento remoto e de sistema de informação geográfica (SIG). A série temporal analisada foi de 1973 a 2005, utilizando-se imagens de satélite obtidas de diferentes sensores (MSS/LANDSAT-1 - 1973; TM/LANDSAT-5 - 1988; TM/LANDSAT-5 - 1999; ETM+/LANSAT-7 - 2001 e CCD/CBERS-2 - 2005). Para o mapeamento dos manguezais, considerou-se apenas as áreas cobertas por vegetação de mangue, sendo o apicum classificado com uma classe distinta da classe manguezal. O estudo identificou que a extensão e a distribuição da floresta de mangue variaram bastante ao longo das últimas três décadas na área estudada, com diferentes fases, caracterizadas pelo incremento e redução da cobertura de mangue. Os resultados obtidos foram: 1973 = 7.101ha, 1988 = 5.821ha, 1999 = 6.442ha, 2001= 6.187ha e 2005 = 6.575ha. As áreas ocupadas por viveiros também foram avaliadas e apresentaram um crescimento exponencial, sendo este, mais acentuado nos anos recentes, com os seguintes valores: 1973 = 0ha, 1988 = 7ha, 1999 = 366ha, 2001 = 1.157ha e 2005 = 1.474ha. O estudo mostrou que entre os anos de 1973 e 2005, houve uma redução de 2.052ha de mangue, dos quais 197ha foram reduzidos pela conversão em viveiros de cultivo. Desta forma, é possível afirmar que a real contribuição da carcinicultura na conversão dessas áreas foi 9,6% do total de área reduzida e que outras atividades de origem antrópica, como agricultura, a expansão urbana e o turismo foram as principais responsáveis por este cenário de redução de áreas de mangue no litoral norte do estado de Pernambuco.
19
Niu, Xin. "Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping." Doctoral thesis, KTH, Geodesi och geoinformatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104762.
Анотація:
Urban land cover mapping represents one of the most important remote sensing applications in the context of rapid global urbanization. In recent years, high resolution spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) has been increasingly used for urban land cover/land-use mapping, since more information could be obtained in multiple polarizations and the collection of such data is less influenced by solar illumination and weather conditions. The overall objective of this research is to develop effective methods to extract accurate and detailed urban land cover information from spaceborne PolSAR data. Six RADARSAT-2 fine-beam polarimetric SAR and three RADARSAT-2 ultra-fine beam SAR images were used. These data were acquired from June to September 2008 over the north urban-rural fringe of the Greater Toronto Area, Canada. The major landuse/land-cover classes in this area include high-density residential areas, low-density residential areas, industrial and commercial areas, construction sites, roads, streets, parks, golf courses, forests, pasture, water and two types of agricultural crops. In this research, various polarimetric SAR parameters were evaluated for urban land cover mapping. They include the parameters from Pauli, Freeman and Cloude-Pottier decompositions, coherency matrix, intensities of each polarization and their logarithms. Both object-based and pixel-based classification approaches were investigated. Through an object-based Support Vector Machine (SVM) and a rule-based approach, efficiencies of various PolSAR features and the multitemporal data combinations were evaluated. For the pixel-based approach, a contextual Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) and a modified Multiscale Pappas Adaptive Clustering (MPAC), contextual information was explored to improve the mapping results. To take full advantages of alternative PolSAR distribution models, a rule-based model selection approach was put forward in comparison with a dictionary-based approach. Moreover, the capability of multitemporal fine-beam PolSAR data was compared with multitemporal ultra-fine beam C-HH SAR data. Texture analysis and a rule-based approach which explores the object features and the spatial relationships were applied for further improvement. Using the proposed approaches, detailed urban land-cover classes and finer urban structures could be mapped with high accuracy in contrast to most of the previous studies which have only focused on the extraction of urban extent or the mapping of very few urban classes. It is also one of the first comparisons of various PolSAR parameters for detailed urban mapping using an object-based approach. Unlike other multitemporal studies, the significance of complementary information from both ascending and descending SAR data and the temporal relationships in the data were the focus in the multitemporal analysis. Further, the proposed novel contextual analyses could effectively improve the pixel-based classification accuracy and present homogenous results with preserved shape details avoiding over-averaging. The proposed contextual SEM algorithm, which is one of the first to combine the adaptive MRF and the modified MPAC, was able to mitigate the degenerative problem in the traditional EM algorithms with fast convergence speed when dealing with many classes. This contextual SEM outperformed the contextual SVM in certain situations with regard to both accuracy and computation time. By using such a contextual algorithm, the common PolSAR data distribution models namely Wishart, G0p, Kp and KummerU were compared for detailed urban mapping in terms of both mapping accuracy and time efficiency. In the comparisons, G0p, Kp and KummerU demonstrated better performances with higher overall accuracies than Wishart. Nevertheless, the advantages of Wishart and the other models could also be effectively integrated by the proposed rule-based adaptive model selection, while limited improvement could be observed by the dictionary-based selection, which has been applied in previous studies. The use of polarimetric SAR data for identifying various urban classes was then compared with the ultra-fine-beam C-HH SAR data. The grey level co-occurrence matrix textures generated from the ultra-fine-beam C-HH SAR data were found to be more efficient than the corresponding PolSAR textures for identifying urban areas from rural areas. An object-based and pixel-based fusion approach that uses ultra-fine-beam C-HH SAR texture data with PolSAR data was developed. In contrast to many other fusion approaches that have explored pixel-based classification results to improve object-based classifications, the proposed rule-based fusion approach using the object features and contextual information was able to extract several low backscatter classes such as roads, streets and parks with reasonable accuracy.QC 20121112
20
Niu, Xin. "Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping." Licentiate thesis, KTH, Geodesi och geoinformatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-31176.
Анотація:
Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand. This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study. Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm. Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one.QC 20110315
21
MOTA, GUILHERME LUCIO ABELHA. "KNOWLEDGE BASED INTERPRETATION APPLIED TO MULTITEMPORAL LOW RESOLUTION SATELLITE IMAGES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5483@1.
Анотація:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORDEUTSCHER AKADEMISCHER AUSTAUSCHDIENST
A presente tese investiga a representação explícita de conhecimento específico na interpretação de imagens de baixa resolução multitemporais adquiridas por satélite. Neste contexto, o termo conhecimento específico, se refere a todo e qualquer tipo de conhecimento que torna um indivíduo capaz de ou mais apto para realizar uma determinada tarefa. Dentro do escopo desta tese, conhecimento específico compreende o conjunto das informações necessárias para a interpretação de imagens de satélite de baixa resolução, como por exemplo: as características das classes presentes, o manejo agronômico e a ecologia da região de interesse. Assim sendo, a presente tese propõe um modelo para a interpretação baseada em conhecimento de imagens de satélite de baixa resolução visando reproduzir o raciocínio empregado pelo foto-intéprete ao realizar a interpretação visual. Neste modelo são empregadas diferentes formas de conhecimento específico: 1) Conhecimento espectral que associa as diversas assinaturas espectrais observadas na imagem de entrada às classes da legenda, agrupando em uma única classe espectral as classes da legenda cujas assinaturas espectrais sejam de difícil discriminação. 2) Conhecimento contextual que indica os diversos contextos relevantes para a discriminação de classes da legenda com assinaturas espectrais semelhantes. 3) Conhecimento multitemporal que relaciona, considerando a classificação anterior, as classificações possíveis no presente momento e a possibilidade de ocorrência de cada uma delas. A potencialidade desta abordagem foi avaliada através de uma série de experimentos, onde, como base de dados, são utilizadas imagens de duas regiões inseridas na Alta Bacia do Rio Taquari ao leste do pantanal mato-grossense. O objetivo primordial destes experimentos foi explicitar a contribuição de cada forma de conhecimento. Os resultados obtidos foram animadores e indicam que o uso de abordagens baseadas em conhecimento pode automatizar grande parte do processo de fotointerpretação, aumentando a produtividade dos foto-intérpretes. No futuro, os resultados da presente pesquisa contribuirão para a construção de sistemas capazes de realizar uma estratégia de interpretação qualquer a ser definida pelo próprio foto-intérprete, acelerando o monitoramento do uso do solo com base em imagens de baixa resolução adquiridas por satélite.
The present thesis investigates the explicit representation of specific knowledge for the automatic interpretation of multitemporal low resolution satellite images. In this context, the term specific knowledge refers to all and any type of knowledge that makes an individual capable or more competent to carry out one determined task. In the scope of this thesis, specific knowledge comprehends the necessary information for the interpretation of low resolution satellite images, for instance: the characteristics of the classes in the legend, the agronomic management, and the ecology of the region under interest. Thus, the present thesis proposes a framework for the knowledge based interpretation of low-resolution satellite images which concerns at reproducing the reasoning used by the photo-interpreter while performing the visual interpretation. This model employs three different kinds of specific knowledge: 1) Spectral knowledge, that associates the diverse observed spectral signatures in the input image to the correspondent classes in the legend, grouping under a single spectral class the classes of the legend whose spectral signatures are difficult to be discriminated. 2) Contextual knowledge, which indicates the diverse contexts for the discrimination of the classes in the legend with similar spectral signatures. 3) Multitemporal knowledge, which relates, considering the previous classification, the possible classifications at the present moment and their respective possibility of occurrence. The potentiality of this methodology was evaluated through a series of experiments. The dataset consisted of images of two regions inserted in the Upper Watershed of the Taquari River, situated at the east of the Brazilian Pantanal, a lowlands ecological sanctuary located in the States of Mato Grosso and Mato Grosso do Sul. The main objective of the experiments was to evaluate the contribution of each sort of knowledge. The results indicate that the use of knowledge based methods can automate great part of the interpretation process, increasing the productivity of the photointerpreters. In the future, the results of the present research can guide the development of systems capable to automatically perform any interpretation strategy, defined by the proper photointerpreter, speeding up the monitoring of land use based on low resolution satellite images.
22
Crusco, Natália de Almeida. "Sensoriamento remoto para análise multitemporal da dinâmica de áreas agrícolas." Instituto Nacional de Pesquisas Espaciais, 2006. http://urlib.net/sid.inpe.br/MTC-m13@80/2006/08.25.14.03.
Анотація:
Estatísticas agrícolas são importantes em um país como o Brasil, onde a agricultura desempenha um papel fundamental na economia brasileira. As metodologias atualmente utilizadas são baseadas em dados subjetivos, e apresentam um caráter nãoprobabilístico. No intuito de aprimorar os resultados obtidos, o projeto Geosafras utiliza dados de sensoriamento remoto associado a dados de campo na geração de estimativa de áreas agrícolas para as principais culturas existentes no país. Porém, este método apresenta algumas limitações quanto à validação dos dados provenientes do campo. Neste sentido, este trabalho tem como hipótese central a existência de relação entre os dados coletados em campo no presente e informações de uso do solo em tempos passados. Assim, o principal objetivo é avaliar como a dinâmica de áreas agrícolas, pela abordagem multitemporal, pode auxiliar o processo de previsão, auditoria e validação de dados de campo. O estudo de imagens multitemporais possibilitou a avaliação da dinâmica de áreas agrícolas e do padrão de uso do solo na área de estudo. Foram utilizadas 24 imagens dos sensores TM/Landsat-5 e ETM+/Landsat-7 no período de 2002 a 2005. As classes avaliadas neste trabalho - soja, cana-de-açúcar, pasto e mata - foram bem discriminadas visual e espectralmente. A análise da dinâmica temporal mostrou que cada classe possui padrões distintos, que estão associados também ao calendário agrícola da região. A metodologia empregada neste trabalho foi eficiente tanto na realização da previsão de uso do solo, como na indicação dos pontos a serem auditados no painel amostral do projeto Geosafras, apontando os erros que podem ser cometidos em campo e depurados pela utilização das imagens de satélite.Agricultural statistics are important in a country like Brazil, where agriculture plays an important role over the economy. The methodologies for crop area estimates are commonly based on subjective data, and they present a non-probabilistic profile. In order to increase the quality of the results, the Geosafras Project uses remote sensing data associated to field data for estimating the area of agricultural crops for the main crop types existing in the country. However, this method presents some limitations regarding the field data validation. This work tackled this aspect and has as central hypothesis the existence of relation between the field data colleted in the present and information about the land-use in the past. Thus, the main objective is to evaluate how the agricultural crop land use dynamics, evaluated here by remote sensing multitemporal analysis, can assist the early estimation process, auditing and field data validation. The analysis of multitemporal images showed that it was possible the validation of the agricultural land dynamics and the land-use patterns in the study area. In order to accomplish the study, 24 TM/Landsat-5 and ETM+/Landsat-7 images in the time-frame from 2002 to 2005 were used. The crop land use classes evaluated in this work soybean, sugarcane, grassland and forest were well distinguished visually and spectrally. The analysis of the temporal dynamics showed that each class has a distinct pattern, which is also associated to the agricultural schedule/calendar of the region. The methodology used in this work was efficient for the land-use prediction, as well as for the indication of the plotted points to be audited in the sample panel of the Geosafras Project. Also, it was possible to identify the errors that can be committed during field sampling and corrected them by using multitemporal satellite images.
23
Liu, Sicong. "Advanced Techniques for Automatic Change Detection in Multitemporal Hyperspectral Images." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/368616.
Анотація:
The increasing availability of the new generation remote sensing satellite hyperspectral images provides an important data source for Earth Observation (EO). Hyperspectral images are characterized by a very detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range. This important property makes it possible the monitoring of the land-cover dynamic and environmental evolution at a fine spectral scale. This also allows one to potentially detect subtle spectral variations associated with the land-cover transitions that are usually not detectable in the traditional multispectral images due to their poor spectral signature representation (i.e., generally sufficient for representing only the major changes). To fully utilize the available multitemporal hyperspectral images and their rich information content, it is necessary to develop advanced techniques for robust change detection (CD) in multitemporal hyperspectral images, thus to automatically discover and identify the interesting and valuable change information. This is the main goal of this thesis.
In the literature, most of the CD approaches were designed for multispectral images. The effectiveness of these approaches, to the complex CD problems is reduced, when dealing with the hyperspectral images. Accordingly, the research activities carried out during this PhD study and presented in this thesis are devoted to the development of effective methods for multiple change detection in multitemporal hyperspectral images. These methods consider the intrinsic properties of the hyperspectral data and overcome the drawbacks of the existing CD techniques. In particular, the following specific novel contributions are introduced in this thesis:
1) A theoretical and empirical analysis of the multiple-change detection problem in multitemporal hyperspectral images. Definition and discussion of concepts as the changes and of the change endmembers, the hierarchical change structure and the multitemporal spectral mixture is given.
2) A novel semi-automatic sequential technique for iteratively discovering, visualizing, and detecting multiple changes. Reliable change variables are adaptively generated for the representation of each specific considered change. Thus multiple changes are discovered and discriminated according to an iterative re-projection of the spectral change vectors into new compressed change representation domains. Moreover, a simple yet effective tool is developed allowing user to have an interaction within the CD procedure.
3) A novel partially-unsupervised hierarchical clustering technique for the separation and identification of multiple changes. By considering spectral variations at different processing levels, multiple change information is adaptively modelled and clustered according to spectral homogeneity. A manual initialization is used to drive the whole hierarchical clustering procedure;
4) A novel automatic multitemporal spectral unmixing approach to detect multiple changes in hyperspectral images. A multitemporal spectral mixture model is proposed to analyse the spectral variations at sub-pixel level, thus investigating in details the spectral composition of change and no-change endmembers within a pixel. A patch-scheme is used in the endmembers extraction and unmixing, which better considers endmember variability.
Comprehensive qualitative and quantitative experimental results obtained on both simulated and real multitemporal hyperspectral images confirm the effectiveness of the proposed techniques.
24
Liu, Sicong. "Advanced Techniques for Automatic Change Detection in Multitemporal Hyperspectral Images." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1393/1/Thesis-SicongLiu.pdf.
Анотація:
The increasing availability of the new generation remote sensing satellite hyperspectral images provides an important data source for Earth Observation (EO). Hyperspectral images are characterized by a very detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range. This important property makes it possible the monitoring of the land-cover dynamic and environmental evolution at a fine spectral scale. This also allows one to potentially detect subtle spectral variations associated with the land-cover transitions that are usually not detectable in the traditional multispectral images due to their poor spectral signature representation (i.e., generally sufficient for representing only the major changes). To fully utilize the available multitemporal hyperspectral images and their rich information content, it is necessary to develop advanced techniques for robust change detection (CD) in multitemporal hyperspectral images, thus to automatically discover and identify the interesting and valuable change information. This is the main goal of this thesis.
In the literature, most of the CD approaches were designed for multispectral images. The effectiveness of these approaches, to the complex CD problems is reduced, when dealing with the hyperspectral images. Accordingly, the research activities carried out during this PhD study and presented in this thesis are devoted to the development of effective methods for multiple change detection in multitemporal hyperspectral images. These methods consider the intrinsic properties of the hyperspectral data and overcome the drawbacks of the existing CD techniques. In particular, the following specific novel contributions are introduced in this thesis:
1) A theoretical and empirical analysis of the multiple-change detection problem in multitemporal hyperspectral images. Definition and discussion of concepts as the changes and of the change endmembers, the hierarchical change structure and the multitemporal spectral mixture is given.
2) A novel semi-automatic sequential technique for iteratively discovering, visualizing, and detecting multiple changes. Reliable change variables are adaptively generated for the representation of each specific considered change. Thus multiple changes are discovered and discriminated according to an iterative re-projection of the spectral change vectors into new compressed change representation domains. Moreover, a simple yet effective tool is developed allowing user to have an interaction within the CD procedure.
3) A novel partially-unsupervised hierarchical clustering technique for the separation and identification of multiple changes. By considering spectral variations at different processing levels, multiple change information is adaptively modelled and clustered according to spectral homogeneity. A manual initialization is used to drive the whole hierarchical clustering procedure;
4) A novel automatic multitemporal spectral unmixing approach to detect multiple changes in hyperspectral images. A multitemporal spectral mixture model is proposed to analyse the spectral variations at sub-pixel level, thus investigating in details the spectral composition of change and no-change endmembers within a pixel. A patch-scheme is used in the endmembers extraction and unmixing, which better considers endmember variability.
Comprehensive qualitative and quantitative experimental results obtained on both simulated and real multitemporal hyperspectral images confirm the effectiveness of the proposed techniques.
25
Marin, Carlo. "Advanced methods for change detection in VHR multitemporal SAR images." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/368566.
Анотація:
Change detection aims at identifying possible changes in the state of an object or phenomenon by jointly observing data acquired at different times over the same geographical
area. In this context, the repetitive coverage and high quality of remotely sensed images acquired by Earth-orbiting satellites make such kind of data an ideal information source for change detection. Among the different kinds of Earth-observation systems, here we focus on Synthetic Aperture Radar (SAR). Differently from optical sensors, SAR is able to regularly monitor the Earth surface independently from the presence of cloud cover or sunlight illumination, making SAR data very attractive from an operational point of view. A new generation of SAR systems such as TerraSAR-X, TANDEM-X and COSMO-SkyMed, which are able to acquired data with a Very High geometrical Resolution (VHR),
has opened new attractive opportunities to study dynamic phenomena that occur on the Earth surface. Nevertheless, the high amount of geometrical details has brought several
challenging issues related to the data analysis that should be addressed. Indeed, even though in the literature several techniques have been developed for the automatic analysis
of multitemporal low- and medium-resolution SAR data, they are poorly effective when dealing with VHR images. In detail, in this thesis we aim at developing advanced methods
for change detection that are able to properly exploit the characteristics of VHR SAR images. i) An approach to building change detection. The approach is based on a novel theoretical model of backscattering that describes the appearance of new or fully collapsed buildings. The use of a fuzzy rule set allows in real scenarios an efficient and effective detection of new/collapsed building among several other sources of changes. ii) A change detection approach for the identification of damages in urban areas after
catastrophic events such as earthquakes or tsunami. The approach is based on two steps: first the most damaged urban areas over a large territory are detected by analyzing high resolution stripmap SAR images. These areas drive the acquisition of new VHR spotlight images, which are used in the second step of the approach to accurately identify collapsed buildings. iii) An approach for surveillance applications. The proposed strategy detects the changes
of interest over important sites such as ports and airports by performing a hierarchical multiscale analysis of the multitemporal SAR images based on a Wavelet decomposi-
tion technique. iv) An approach to multitemporal primitive detection. The approach, based on the Bayesian rule for compound classification integrated in a fuzzy inference system, takes advantage of the multitemporal correlation of images pairs in order to both improve the detection of the primitives and identify the changes in their state.
For each of the above mentioned topic an analysis of the state of the art is carried out, the limitations of existing methods are pointed out and the proposed solutions to the considered problems are described in details. Experimental results conducted on simulated and real remote sensing data are provided in order to show and confirm the validity of
each of the proposed methods.
26
Marin, Carlo. "Advanced methods for change detection in VHR multitemporal SAR images." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1435/1/PhD-Thesis.pdf.
Анотація:
Change detection aims at identifying possible changes in the state of an object or phenomenon by jointly observing data acquired at different times over the same geographical
area. In this context, the repetitive coverage and high quality of remotely sensed images acquired by Earth-orbiting satellites make such kind of data an ideal information source for change detection. Among the different kinds of Earth-observation systems, here we focus on Synthetic Aperture Radar (SAR). Differently from optical sensors, SAR is able to regularly monitor the Earth surface independently from the presence of cloud cover or sunlight illumination, making SAR data very attractive from an operational point of view. A new generation of SAR systems such as TerraSAR-X, TANDEM-X and COSMO-SkyMed, which are able to acquired data with a Very High geometrical Resolution (VHR),
has opened new attractive opportunities to study dynamic phenomena that occur on the Earth surface. Nevertheless, the high amount of geometrical details has brought several
challenging issues related to the data analysis that should be addressed. Indeed, even though in the literature several techniques have been developed for the automatic analysis
of multitemporal low- and medium-resolution SAR data, they are poorly effective when dealing with VHR images. In detail, in this thesis we aim at developing advanced methods
for change detection that are able to properly exploit the characteristics of VHR SAR images. i) An approach to building change detection. The approach is based on a novel theoretical model of backscattering that describes the appearance of new or fully collapsed buildings. The use of a fuzzy rule set allows in real scenarios an efficient and effective detection of new/collapsed building among several other sources of changes. ii) A change detection approach for the identification of damages in urban areas after
catastrophic events such as earthquakes or tsunami. The approach is based on two steps: first the most damaged urban areas over a large territory are detected by analyzing high resolution stripmap SAR images. These areas drive the acquisition of new VHR spotlight images, which are used in the second step of the approach to accurately identify collapsed buildings. iii) An approach for surveillance applications. The proposed strategy detects the changes
of interest over important sites such as ports and airports by performing a hierarchical multiscale analysis of the multitemporal SAR images based on a Wavelet decomposi-
tion technique. iv) An approach to multitemporal primitive detection. The approach, based on the Bayesian rule for compound classification integrated in a fuzzy inference system, takes advantage of the multitemporal correlation of images pairs in order to both improve the detection of the primitives and identify the changes in their state.
For each of the above mentioned topic an analysis of the state of the art is carried out, the limitations of existing methods are pointed out and the proposed solutions to the considered problems are described in details. Experimental results conducted on simulated and real remote sensing data are provided in order to show and confirm the validity of
each of the proposed methods.
27
Solano-Correa, Yady Tatiana. "Advanced methods for the analysis of multispectral multitemporal satellite images." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/369046.
Анотація:
Thanks to the revisit property of the Earth observation satellites, a huge amount of multitemporal (MT) images are now available in archives. Such kind of images allows us to monitor land surface changes in wide geographical areas according to both long term (e.g., yearly) and short term (e.g., daily) observations. Evolution on the acquisition sensor technology has resulted in the availability of MT and multispectral satellite images with: i) Very High spatial Resolution (VHR) (e.g., Quick-Bird, WorlView-2) and, ii) very high temporal and high spatial resolutions (e.g., Sentinel-2 (S2)). Images acquired by such sensors allow for a detailed geometrical and temporal analysis when compared to medium or high spatial and temporal resolution data. Nevertheless, factors like satel-lite revisit period, the possible competing orders of different users on the satellite pointing (for VHR images only), the limited life of a satellite mission, and weather conditions can lead to: i) lack of enough images acquired by a single sensor to perform MT analysis (VHR case), and ii) lack of regular and continuous Time Series (TS) to perform short term MT analysis (very high temporal and high spatial resolution sensors case) at the level of small objects. Both problems arise from the application requirements on temporal resolution and are being of particular interest in the last years. Two main solutions to the above mentioned problems can be considered: i) use of multisen-sor VHR optical images to replace the missing acquisitions when a single VHR sensor is considered and; ii) development of regression techniques to reconstruct regular and continuous TS from both high temporal and very high temporal resolution sensors. In the literature, most of the MT analysis techniques have been designed to work with: i) VHR images acquired by single sensors, and ii) multispectral images acquired by high spatial resolution sensors, but with low temporal resolution or very high temporal resolution, but with low spatial resolution. Therefore, the effectiveness of ex-isting techniques when applied to the complex MT problems in both VHR multisensor and very high temporal resolution images is reduced. Accordingly, the goal of this thesis is to develop novel tech-niques for the automatic analysis of MT multispectral satellite images such that images acquired by multisensor VHR and very high temporal resolution sensors can be analyzed.
The thesis provides four main novel contributions to the state-of-the-art. The first three contribu-tions address the problems arising from the analysis of multisensor VHR multispectral images, whereas the fourth one deals with the problems faced while working with long TS acquired by sen-sors with high spatial and very high temporal resolutions. The first contribution presents an ap-proach for unsupervised CD in multisensor MT VHR images, where the possible sources of noise/changes are studied in detail and a strategy to mitigate them at the levels of pre-processing and feature extraction is presented. In the second contribution, further attention is paid to the ho-mogenization step and a method to generate homogeneous VHR TS focused on the homogenization of intrinsic spectral induced differences is presented. The third contribution further focuses on the detection of multiple changes, while relaxing the knowledge on the statistical distribution of the classes. To this aim, a method based on iterative clustering and adaptive thresholding is imple-mented. Comprehensive qualitative and quantitative experimental results, with real VHR multisen-sor datasets, confirm the effectiveness of the proposed approaches and led to the development of 3 contributions that allow to perform unsupervised bi-temporal CD by means of MT multisensor VHR images.
In the fourth contribution, an approach to handle images acquired by both high spatial and very high temporal resolution sensors is presented. To this aim, spectral, spatial and temporal infor-mation of S2 satellite images TS is exploited in different phases and in a fully automatic way, allow-ing for the derivation of different relevant products in the precision agriculture field. Comprehen-sive qualitative and, to some extent, quantitative experimental results confirm the capacity of the method to automatically exploit TS containing both high spatial and very high temporal resolution information. The proposed method can be easily extrapolated for other applications and other sen-sors with similar characteristics to those of S2.
28
Solano-Correa, Yady Tatiana. "Advanced methods for the analysis of multispectral multitemporal satellite images." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2845/1/PhD-Thesis-SolanoCorreaYadyTatiana.pdf.
Анотація:
Thanks to the revisit property of the Earth observation satellites, a huge amount of multitemporal (MT) images are now available in archives. Such kind of images allows us to monitor land surface changes in wide geographical areas according to both long term (e.g., yearly) and short term (e.g., daily) observations. Evolution on the acquisition sensor technology has resulted in the availability of MT and multispectral satellite images with: i) Very High spatial Resolution (VHR) (e.g., Quick-Bird, WorlView-2) and, ii) very high temporal and high spatial resolutions (e.g., Sentinel-2 (S2)). Images acquired by such sensors allow for a detailed geometrical and temporal analysis when compared to medium or high spatial and temporal resolution data. Nevertheless, factors like satel-lite revisit period, the possible competing orders of different users on the satellite pointing (for VHR images only), the limited life of a satellite mission, and weather conditions can lead to: i) lack of enough images acquired by a single sensor to perform MT analysis (VHR case), and ii) lack of regular and continuous Time Series (TS) to perform short term MT analysis (very high temporal and high spatial resolution sensors case) at the level of small objects. Both problems arise from the application requirements on temporal resolution and are being of particular interest in the last years. Two main solutions to the above mentioned problems can be considered: i) use of multisen-sor VHR optical images to replace the missing acquisitions when a single VHR sensor is considered and; ii) development of regression techniques to reconstruct regular and continuous TS from both high temporal and very high temporal resolution sensors. In the literature, most of the MT analysis techniques have been designed to work with: i) VHR images acquired by single sensors, and ii) multispectral images acquired by high spatial resolution sensors, but with low temporal resolution or very high temporal resolution, but with low spatial resolution. Therefore, the effectiveness of ex-isting techniques when applied to the complex MT problems in both VHR multisensor and very high temporal resolution images is reduced. Accordingly, the goal of this thesis is to develop novel tech-niques for the automatic analysis of MT multispectral satellite images such that images acquired by multisensor VHR and very high temporal resolution sensors can be analyzed.
The thesis provides four main novel contributions to the state-of-the-art. The first three contribu-tions address the problems arising from the analysis of multisensor VHR multispectral images, whereas the fourth one deals with the problems faced while working with long TS acquired by sen-sors with high spatial and very high temporal resolutions. The first contribution presents an ap-proach for unsupervised CD in multisensor MT VHR images, where the possible sources of noise/changes are studied in detail and a strategy to mitigate them at the levels of pre-processing and feature extraction is presented. In the second contribution, further attention is paid to the ho-mogenization step and a method to generate homogeneous VHR TS focused on the homogenization of intrinsic spectral induced differences is presented. The third contribution further focuses on the detection of multiple changes, while relaxing the knowledge on the statistical distribution of the classes. To this aim, a method based on iterative clustering and adaptive thresholding is imple-mented. Comprehensive qualitative and quantitative experimental results, with real VHR multisen-sor datasets, confirm the effectiveness of the proposed approaches and led to the development of 3 contributions that allow to perform unsupervised bi-temporal CD by means of MT multisensor VHR images.
In the fourth contribution, an approach to handle images acquired by both high spatial and very high temporal resolution sensors is presented. To this aim, spectral, spatial and temporal infor-mation of S2 satellite images TS is exploited in different phases and in a fully automatic way, allow-ing for the derivation of different relevant products in the precision agriculture field. Comprehen-sive qualitative and, to some extent, quantitative experimental results confirm the capacity of the method to automatically exploit TS containing both high spatial and very high temporal resolution information. The proposed method can be easily extrapolated for other applications and other sen-sors with similar characteristics to those of S2.
29
Bertoluzza, Manuel. "Novel Methods for Change Detection in Multitemporal Remote Sensing Images." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/368310.
Анотація:
The scope of this dissertation is to present and discuss novel paradigms and techniques for the extraction of information from long time series of remotely sensed images.
Many images are acquired everyday at high spatial and temporal resolution. The unprecedented availability of images is increasing due to the number of acquiring sensors. Nowadays, many satellites have been launched in orbit around our planet and more launches are planned in the future. Notable examples of currently operating remote sensing missions are the Landsat and Sentinel programs run by space agencies. This trend is speeding up every year with the launch of many other commercial satellites. Initiatives like cubesats propose a new paradigm to continuously monitor Earth’s surface. The larger availability of remotely sensed data does not only involve space-borne platforms. In the recent years, new platforms, such as airborne unmanned vehicles, gained popularity also thanks to the reduction of costs of these instruments. Overall, all these phenomena are fueling the so-called Big Data revolution in remote sensing. The unprecedented number of images enables a large number of applications related to the monitoring of the environment on a global and regional scale. A non-exhaustive list of applications contains climate change assessment, disaster monitoring and urban planning.
In this thesis, novel paradigms and techniques are proposed for the automatic exploitation of the information acquired by the growing number of remote sensing data sources, either multispectral or Synthetic Aperture Radar (SAR) sensors. There is a need of new processing strategies being able to reliably and automatically extract information from the ever growing amount of images. In this context, this thesis focuses on Change Detection (CD) techniques capable of identifying areas within remote sensing images where the land-cover/land-use changed. Indeed, CD is one of the first steps needed to understand Earth’s surface dynamics and its evolution. Images from such long and dense time series have redundant information. So, the information extracted from one image or a single image pair in the time series is correlated to other images or image pairs. This thesis explores mechanisms to exploit the temporal correlation within long image time series for an improved information extraction. This concept is general and can be applied to any information extraction process.
The thesis provides three main novel contributions to the state of the art.
The first contribution consists in a novel framework for CD in image time series. The binary change variable is modeled as a conservative field. Then, it is used to improve the bi-temporal CD map computed between a target pair of images extracted from a time series. This framework takes advantage of the correlation of changes detected between pairs of images extracted from long time series.
The second contribution presents an iterative approach that aims at improving the global CD performance for any possible pair of images defined within a time series. The results obtained by any bi-temporal technique, either binary or multiclass, are automatically validated against each other. By means of an iterative mechanism, the consistency of changes is tested and enforced for any pair of images.
The third contribution consists in the detection of clouds in long time series of multispectral images and in the restoration of pixels covered by clouds. The presence of clouds may strongly affect the automatic analysis of images and the performance of change detection techniques (or other processes for the extraction of information). In this contribution, the temporal information of long optical image time series is exploited to improve the identification of pixels covered by clouds and their restoration with respect to standard monotemporal approaches.
The effectiveness of the proposed approaches is proved on experiments on synthetic and real multispectral and SAR images. Experimental results are accompanied by comprehensive qualitative and quantitative analysis.
30
Bertoluzza, Manuel. "Novel Methods for Change Detection in Multitemporal Remote Sensing Images." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3658/1/thesis_bertoluzza_final_2019-05-02.pdf.
Анотація:
The scope of this dissertation is to present and discuss novel paradigms and techniques for the extraction of information from long time series of remotely sensed images.
Many images are acquired everyday at high spatial and temporal resolution. The unprecedented availability of images is increasing due to the number of acquiring sensors. Nowadays, many satellites have been launched in orbit around our planet and more launches are planned in the future. Notable examples of currently operating remote sensing missions are the Landsat and Sentinel programs run by space agencies. This trend is speeding up every year with the launch of many other commercial satellites. Initiatives like cubesats propose a new paradigm to continuously monitor Earth’s surface. The larger availability of remotely sensed data does not only involve space-borne platforms. In the recent years, new platforms, such as airborne unmanned vehicles, gained popularity also thanks to the reduction of costs of these instruments. Overall, all these phenomena are fueling the so-called Big Data revolution in remote sensing. The unprecedented number of images enables a large number of applications related to the monitoring of the environment on a global and regional scale. A non-exhaustive list of applications contains climate change assessment, disaster monitoring and urban planning.
In this thesis, novel paradigms and techniques are proposed for the automatic exploitation of the information acquired by the growing number of remote sensing data sources, either multispectral or Synthetic Aperture Radar (SAR) sensors. There is a need of new processing strategies being able to reliably and automatically extract information from the ever growing amount of images. In this context, this thesis focuses on Change Detection (CD) techniques capable of identifying areas within remote sensing images where the land-cover/land-use changed. Indeed, CD is one of the first steps needed to understand Earth’s surface dynamics and its evolution. Images from such long and dense time series have redundant information. So, the information extracted from one image or a single image pair in the time series is correlated to other images or image pairs. This thesis explores mechanisms to exploit the temporal correlation within long image time series for an improved information extraction. This concept is general and can be applied to any information extraction process.
The thesis provides three main novel contributions to the state of the art.
The first contribution consists in a novel framework for CD in image time series. The binary change variable is modeled as a conservative field. Then, it is used to improve the bi-temporal CD map computed between a target pair of images extracted from a time series. This framework takes advantage of the correlation of changes detected between pairs of images extracted from long time series.
The second contribution presents an iterative approach that aims at improving the global CD performance for any possible pair of images defined within a time series. The results obtained by any bi-temporal technique, either binary or multiclass, are automatically validated against each other. By means of an iterative mechanism, the consistency of changes is tested and enforced for any pair of images.
The third contribution consists in the detection of clouds in long time series of multispectral images and in the restoration of pixels covered by clouds. The presence of clouds may strongly affect the automatic analysis of images and the performance of change detection techniques (or other processes for the extraction of information). In this contribution, the temporal information of long optical image time series is exploited to improve the identification of pixels covered by clouds and their restoration with respect to standard monotemporal approaches.
The effectiveness of the proposed approaches is proved on experiments on synthetic and real multispectral and SAR images. Experimental results are accompanied by comprehensive qualitative and quantitative analysis.
31
Amaral, Luísa Gurjão de Carvalho. "Incremento de carbono estocado na parte aérea de plantios de restauração em corredores integrando unidades de conservação e fragmentos ripários." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11150/tde-03012018-123354/.
Анотація:
Metodologias que sejam padronizadas e consolidadas para quantificar carbono em florestas tropicais vem sendo discutidas em convenções climáticas. Este trabalho contribui para a estimativa de biomassa e carbono estocado na parte aérea de áreas de restauração da Mata Atlântica em torno de reservatórios localizados no Pontal do Paranapanema utilizando tecnologia LiDAR (Light Detection and Ranging). Procurou-se explorar o acúmulo e o estoque de carbono em três florestas em diferentes condições de sucessão: duas florestas caracterizdas como madura e a floresta restaurada. Na primeira etapa do trabalho, houve a escolha das equações alométricas encontradas em literatura para determinar a quantidade de carbono em cada uma das áreas utilizando variáveis medidas em campo. Assim, foi utilizada a equação de Ferez et al. (2015), ajustada em floresta restaurada da Mata Atlântica, para quantificar o carbono da área de estudo. Na segunda etapa do trabalho, procurou-se estimar a taxa de incremento do estoque de carbono da parte aérea, utilizando as variáveis medidas em campo obtidas em duas campanhas, nos anos de 2015 e 2016. O corredor restaurado apresentou média de 7,1 Mg.C.ha-1, a floresta madura da ESEC apresentou 39,9 Mg.C.ha-1, e a floresta madura do Morro do Diabo apresentou 45,2 Mg.C.ha-1 para o ano de 2016. A fixação anual encontrada na variação do estoque de 2015 para 2016 foi de 1,2 Mg.ha-1.ano-1 para a floresta restaurada, 1,6 Mg.ha-1.ano-1 para a floresta madura da ESEC e 2.5 Mg.ha-1.ano-1 para a floresta madura do MD. A terceira etapa do trabalho traz a modelagem realizada com dados LiDAR e dados do inventário convencional. Após utilizados métodos estatísticos para seleção de modelo, coeficiente de determinação ajustado, critério de Akaike e erro padrão da estimativa, o modelo escolhido utiliza as métricas percentil 90 e porcentagem de retornos acima de 50cm do solo para estimar carbono acima do solo, obtendo como coeficiente de determinação 0,78. A extrapolação para a área total pelo método do inventário convencional e pelo método da modelagem LiDAR apresentaram diferenças, demonstrando a utilização do LiDAR para reconhecer e retirar informações de áreas não amostradas. O quarto capítulo dessa dissertação traz a variação de carbono estimado pela modelagem com métricas LiDAR para os anos de 2015 e 2016, além do mapa do estocagem evidenciando os locais de maior sucesso e os de menor. A tecnologia LiDAR se mostrou eficiente em captar a variação ocorrida no intervalo de um ano e na quantificação de carbono.The establishment of methodologies used to quantify carbon in tropical forests are one of the main topics on climate conventions. This project contributes to the estimation of biomass and aboveground carbon stored around reservoirs located in Pontal do Paranapanema, São Paulo - Brazil, using Light Detection and Ranging (LiDAR) technology. The objective was to explore the accumulation of aboveground carbon stored in three different succession conditions: mature, secondary and restored forest. The first chapter of this thesis shows the common allometric equations found in literature used to determine the amount of carbon in each area using field variables. However, the chosen allometric equation was developed by Ferez et al. (2015), because it was adjusted in similar areas than the area of this work. Thus, the restored corridor presented a mean of 7.1 Mg.C.ha-1, the secondary forest 39.9 Mg.C.ha-1 and the mature forest 45.2 Mg.C.ha-1 for the year of 2016. The annual fixation found was about 1.2 Mg.ha-1 for the restored forest, 1.6 Mg.ha-1 for the secondary forest and 2.5 Mg.ha-1 for the mature one. The second article brings the modeling performed with LiDAR data and traditional field inventory data. After using statistical methods for model selection, the chosen model uses two metrics: percentile 90 and percentage of returns above 50 cm of height to estimate aboveground carbon The extrapolation to the total area by traditional inventory and LiDAR modeling showed differences, demonstrating the efficiency of LiDAR to recognize information from non-sampled areas. The last chapter of the thesis brings the variation of carbon using LiDAR data through the years of 2015 and 2016. LiDAR data showed to be useful for measuring aboveground carbon and to detect the increment of the carbon stock over a year.
32
Burkart, Andreas [Verfasser]. "Multitemporal assessment of crop parameters using multisensorial flying platforms / Andreas Burkart." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1096330075/34.
33
Nelson, Marc. "Evaluating Multitemporal Sentinel-2 data for Forest Mapping using Random Forest." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146657.
Анотація:
The mapping of land cover using remotely sensed data is most effective when a robust classification method is employed. Random forest is a modern machine learning algorithm that has recently gained interest in the field of remote sensing due to its non-parametric nature, which may be better suited to handle complex, high-dimensional data than conventional techniques. In this study, the random forest method is applied to remote sensing data from the European Space Agency’s new Sentinel-2 satellite program, which was launched in 2015 yet remains relatively untested in scientific literature using non-simulated data. In a study site of boreo-nemoral forest in Ekerö mulicipality, Sweden, a classification is performed for six forest classes based on CadasterENV Sweden, a multi-purpose land covermapping and change monitoring program. The performance of Sentinel-2’s Multi-SpectralImager is investigated in the context of time series to capture phenological conditions, optimal band combinations, as well as the influence of sample size and ancillary inputs.Using two images from spring and summer of 2016, an overall map accuracy of 86.0% was achieved. The red edge, short wave infrared, and visible red bands were confirmed to be of high value. Important factors contributing to the result include the timing of image acquisition, use of a feature reduction approach to decrease the correlation between spectral channels, and the addition of ancillary data that combines topographic and edaphic information. The results suggest that random forest is an effective classification technique that is particularly well suited to high-dimensional remote sensing data.
34
ROSA, LAURA ELENA CUE LA. "CROP RECOGNITION FROM MULTITEMPORAL SAR IMAGE SEQUENCES USING DEEP LEARNING TECHNIQUES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34919@1.
Анотація:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A presente dissertação tem como objetivo avaliar um conjunto de técnicas de aprendizado profundo para o reconhecimento de culturas agrícolas a partir de sequências multitemporais de imagens SAR. Três métodos foram considerados neste estudo: Autoencoders (AEs), Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs). A avaliação experimental baseou-se em duas bases de dados contendo sequências de imagens geradas pelo sensor Sentinel- 1A. A primeira base cobre uma região tropical e a segunda uma região de clima temperado. Em todos os casos, utilizouse como referência para comparação um classificador Random Forest (RF) operando sobre atributos de textura derivados de matrizes de co-ocorrência. Para a região de clima temperado que apresenta menor dinâmica agrícola as técnicas de aprendizado profundo produziram consistentemente melhores resultados do que a abordagem via RF, sendo AEs o melhor em praticamente todos os experimentos. Na região tropical, onde a dinâmica é mais complexa, as técnicas de aprendizado profundo mostraram resultados similares aos produzidos pelo método RF, embora os quatro métodos tenham se alternado como o de melhor desempenho dependendo do número e das datas das imagens utilizadas nos experimentos. De um modo geral, as RNCs se mostraram mais estáveis do que os outros métodos, atingindo o melhores resultado entre os métodos avaliados ou estando muito próximos destes em praticamente todos os experimentos. Embora tenha apresentado bons resultados, não foi possível explorar todo o potencial das RTCs neste estudo, sobretudo, devido à dificuldade de se balancear o número de amostras de treinamento entre as classes de culturas agrícolas presentes na área de estudo. A dissertação propõe ainda duas estratégias de pós-processamento que exploram o conhecimento prévio sobre a dinâmica das culturas agrícolas presentes na área alvo. Experimentos demonstraram que tais técnicas podem produzir um aumento significativo da acurácia da classificação, especialmente para culturas menos abundantes.
The present dissertation aims to evaluate a set of deep learning (DL) techniques for crop mapping from multitemporal sequences of SAR images. Three methods were considered in this study: Autoencoders (AEs), Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs). The analysis was based on two databases containing image sequences generated by the Sentinel-1A. The first database covers a temperate region that presents a comparatively simpler dynamics, and second database of a tropical region that represents a scenario with complex dynamics. In all cases, a Random Forest (RF) classifier operating on texture features derived from co-occurrence matrices was used as baseline. For the temperate region, DL techniques consistently produced better results than the RF approach, with AE being the best one in almost all experiments. In the tropical region the DL approaches performed similar to RF, alternating as the best performing one for different experimental setups. By and large, CNNs achieved the best or next to the best performance in all experiments. Although the FCNs have performed well, the full potential was not fully exploited in our experiments, mainly due to the difficulty of balancing the number of training samples among the crop types. The dissertation also proposes two post-processing strategies that exploit prior knowledge about the crop dynamics in the target site. Experiments have shown that such techniques can significantly improve the recognition accuracy, in particular for less abundant crops.
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Cabral, Escleide Gomes. "Análise multitemporal da silvicultura no estado de Goiás via sensoriamento remoto." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/7032.
Анотація:
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Forestry is booming in Brazil due to demand for forest products. However, despite the importance of planted forests, forestry in the state of Goiás has been little studied. One of the reasons for the absence of studies in the state is due to the lack of disaggregated data and the great pulverization of the plantations, making it difficult to survey them. This work aimed to know the areas with commercial forest plantations, through remote sensing in the years 2002 and 2013, through the mapping carried out by Probio and TerraClass Cerrado, respectively, and in 2015 by the mapping carried out in this work, to assist in the Planning and development of state forestry. Analyzing the production and the values collected with the forest products in Goiás, we observed that some products have been replaced over the years by others, such as charcoal by wood chips, as well as other products that have been increasing in the production, such as firewood, which in the year 2000 obtained a production of 679,755 m³ and obtained a value of 6.6 million Reals. In less than 15 years, its production increased to 4,357,778 m³ of firewood. When we looked at the 2002, 2013 and the mapping of this study, we realized that there were important advances in the planted areas, so that in 2002 there were just over 500 ha of forest planted in Goiás; In 2013, were approximately 153 thousand ha; And in 2015, the area of forestry was 162,516 ha. The mesoregions with the largest commercial forest plantations are in the South, East and North, and the South and the East are the largest producers of wood and timber in the state of Goiás. The spatial distribution of eucalyptus plantations in Goiás Characterized by plantations in small areas and by a non-vertical production of the producers, reflecting the potential of forestry activity in income generation in small and medium farms and that despite the methodological differences used in the mappings used, the results were close to the results found for the current landscape of the state of Goiás elaborated in this work
A silvicultura está em franca expansão no Brasil devido à demanda pelos produtos florestais. No entanto, apesar da importância das florestas plantadas, a silvicultura no estado de Goiás tem sido pouco estudada. Um dos motivos para a ausência de estudos no estado deve-se à falta de dados desagregados e à grande pulverização dos plantios, dificultando o levantamento dos mesmos. Este trabalho teve como objetivo conhecer as áreas com plantios comerciais de florestas, por meio de sensoriamento remoto nos anos 2002 e 2013, através dos mapeamentos realizados pelo Probio e TerraClass Cerrado, respectivamente, e em 2015 pelo mapeamento realizando nesse trabalho, para que auxiliem no planejamento e desenvolvimento da silvicultura estadual. Analisando a produção e os valores arrecadados com os produtos florestais em Goiás, observamos que alguns produtos vêm sendo substituídos, ao longo dos anos por outros, como, por exemplo, o carvão vegetal por cavacos de madeira, assim como outros produtos que vem apresentando aumento na produção, como a lenha, que no ano 2000 obteve uma produção de 679.755 m³ e conseguiu um valor de 6,6 milhões de reais. Em menos de 15 anos, sua produção passou a ser 4.357.778 m³ de lenha. Ao examinar os mapeamentos de 2002, 2013 e o elaborado neste estudo, percebemos que houve importantes avanços nas áreas plantadas, de forma que, em 2002, havia pouco mais de 500 ha de floresta plantada em Goiás; em 2013, foram aproximadamente 153 mil ha; e, em 2015, a área de silvicultura foi de 162.516 ha. As mesorregiões com as maiores áreas plantio comercial de florestas são a Sul, Leste e Norte, sendo que a Sul e a Leste são as maiores produtoras de lenha e madeira em tora no estado de Goiás. A distribuição espacial dos plantios de eucalipto em Goiás se caracteriza por plantios em pequenas áreas e por uma produção não verticalizada dos produtores, refletindo o potencial da atividade florestal na geração de renda em pequenas e médias propriedades e que apesar das diferenças de metodológicas utilizadas nos mapeamentos utilizados, os resultados foram próximos dos resultados encontrados para a paisagem atual do estado de Goiás elaborada neste trabalho.
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Oliphant, Adam J. "Mapping Elaeagnus Umbellata on Coal Surface Mines using Multitemporal Landsat Imagery." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/75119.
Анотація:
Invasive plant species threaten native plant communities and inhibit efforts to restore disturbed landscapes. Surface coal mines in the Appalachian Mountains are some of the most disturbed landscapes in North America. Moreover, there is not a comprehensive understanding of the land cover characteristics of post- mined lands in Appalachia. Better information on mined lands' vegetative cover and ecosystem recovery status is necessary for implementation of effective environmental management practices. The invasive autumn olive (Elaeagnus umbellata) is abundant on former coal surface mines, often outcompeting native trees due to its faster growth rate. The frequent revisit time and spatial and spectral resolution of Landsat satellites make Landsat imagery well suited for mapping and characterizing land cover and forest recovery on former coal surface mines. I performed a multitemporal classification using a random forest analysis to map autumn olive on former and current surface coal mines in southwest Virginia. Imagery from the Operational Land Imager on Landsat 8 were used as input data for the study. Calibration and validation data for use in model development were obtained using high-resolution aerial imagery. Results indicate that autumn olive cover is sufficiently dense to enable detection using Landsat imagery on approximately 12.6% of the current and former surface coal mines located in the study area that have been mined since the early 1980s. The classified map produced here had a user's and producer's accuracy of 85.3% and 78.6% respectively for the autumn olive coverage class. Overall accuracy in reference to an independent validation dataset was 96.8%. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in the Appalachian coalfields can be mapped using Landsat imagery. Additionally, autumn olive occurrence is a significant landscape feature on former surface coal mines in the Virginia coalfields.Master of Science
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Malambo, Lonesome. "Multitemporal mapping of burned areas in mixed landscapes in eastern Zambia." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71301.
Анотація:
Fires occur extensively across Zambia every year, a problem recognized as a major threat to biodiversity. Yet, basic tools for mapping at a spatial and temporal scale that provide useful information for understanding and managing this problem are not available. The objectives of this research were: to develop a method to map the spatio-temporal seasonal fire occurrence using satellite imagery, to develop a technique for estimating missing data in the satellite imagery considering the possibility of change in land cover over time, and to demonstrate applicability of these new tools by analyzing the fine-scale seasonal patterns of landscape fires in eastern Zambia. A new approach for mapping burned areas uses multitemporal image analysis with a fuzzy clustering algorithm to automatically select spectral-temporal signatures that are then used to classify the images to produce the desired spatio-temporal burned area information. Testing with Landsat data (30m resolution) in eastern Zambia showed accuracies in predicting burned areas above 92%. The approach is simple to implement, data driven, and can be automated, which can facilitate quicker production of burned area information. A profile-based approach for filling missing data uses multitemporal imagery and exploits the similarity in land cover temporal profiles and spatial relationships to reliably estimate missing data even in areas with significant changes. Testing with simulated missing data from an 8-image spectral index sequence showed highly correlated (R2 of 0.78-0.92) and precise estimates (deviations 4-7%) compared to actual values. The profile-based approach overcomes the common requirement of gap-filling methods that there is gradual or no change in land cover, and provides accurate gap-filling under conditions of both gradual and abrupt changes. The spatio-temporal progression of landscape burning was evaluated for the 2009 and 2012 fire seasons (June-November) using Landsat data. Results show widespread burning (~ 60%) with most fires occurring late (August-October) in the season. Fire occurrence and burn patch sizes decreased with increasing settlement density and landscape fragmentation reflecting human influences and fuel availability. Small fires (< 5ha) are predominant and were significantly under-detected (>50%) by a global dataset (MODIS Burned Area Product (500m resolution)), underscoring the critical need of higher geometric resolution imagery such as Landsat imagery for mapping such fine-scale fire activity.Ph. D.
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Brooks, Evan B. "Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23276.
Анотація:
Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation. There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate.
Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements. In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001. Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels. Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels.
We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data. The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm. EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery.
We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values. These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential. These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters. Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase.
These applications demonstrate the utility of harmonic regression for Landsat data. They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects.Ph. D.
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Sobreiro, João Francisco Ferreira. "Vegetation multitemporal responses to hydroclimate variations in the Espinhaço Range (Brazil) /." Rio Claro, 2019. http://hdl.handle.net/11449/183096.
Анотація:
Orientador: Thiago Sanna Freire SilvaResumo: Os sistemas montanhosos são laboratórios naturais para análise de gradientes. Elevação, amplitude e diferenças topográficas em montanhas podem criar fortes diferenças microclimáticas a curtas distâncias, aninhadas dentro da mesma região biogeográfica e macroclimática, permitindo-nos compreender melhor as respostas da vegetação e os feedbacks sobre a disponibilidade de água. Neste estudo, avaliamos como a distribuição da vegetação está ligada à disponibilidade de água na Serra do Espinhaço. Para tanto, abordamos as seguintes questões: 1) Quais são os regimes hidroclimáticos encontrados na Serra do Espinhaço e seus correspondentes tipos de vegetação? 2) Onde a produtividade da vegetação é mais e / ou menos acoplada aos regimes hidroclimáticos? 3) A topografia é capaz de impactar a produtividade da vegetação e suas relações de acoplamento com regimes hidroclimáticos? Além disso, considerando estas relações ambientais e de vegetação, 4) Como a resiliência climática dos tipos de vegetação nesta região varia? Conclui-se que na faixa do Espinhaço, a maior parte da dinâmica de produtividade da vegetação espaço-temporal é impulsionada por condições hidroclimáticas e / ou topo-edáficas. Nossos resultados mostram que a vegetação da Caatinga teve uma resposta plástica e relativamente rápida ao Déficit Hídrico Climático (CWD) e foi o tipo de vegetação com maior restrição hídrica. Cerrado e Campos Rupestres tiveram respostas semelhantes às flutuações no déficit hídrico, mostrando um gradie... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Montane systems are natural laboratories for gradient analysis. Elevation, amplitude and topographical differences over mountains can create strong microclimatic differences over short distances, nested within the same biogeographic and macro-climatic region, thus allowing us to better understand vegetation responses and feedbacks to water availability. In this study, we assessed how vegetation distribution is linked to water availability in the Espinhaço Mountain Range. For that, we addressed the following questions: 1) Which are the hydroclimatic regimes found in the Espinhaço Range and their corresponding vegetation types? 2) Where does vegetation productivity is more and/or less coupled to hydroclimatic regimes? 3) Is topography able to impact vegetation productivity and its coupling relations to hydroclimatic regimes? Also, considering these environmental and vegetation relationships, 4) How does the climatic resilience of the vegetation types in this region vary? We conclude that in the Espinhaço Range, most of the spatio-temporal vegetation productivity dynamics are driven by hydroclimatic and/or topo-edaphic conditions. Our results show that “Caatinga” vegetation had a plastic and relatively fast response to Climatic Water Deficit (CWD) and was the most water-constrained vegetation type. “Cerrado” and “Campos Rupestres” had similar responses to fluctuations in water deficit, showing a gradient of slower to faster responses from “Humid” to “Very dry” hydroclimatic regi... (Complete abstract click electronic access below)
Mestre
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Rezende, Filho José Roberto Gonçalves de. "Análise multitemporal de vegetação em ecossistemas de áreas úmidas utilizando séries temporais derivadas do sensor modis na Ilha do Bananal – Tocantins." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/24034.
Анотація:
Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2017.Submitted by Raquel Almeida (raquel.df13@gmail.com) on 2017-07-11T19:20:59Z No. of bitstreams: 1 2017_JoséRobertoGonçalvesdeRezendeFilho.pdf: 7880125 bytes, checksum: a1dea249205ed56486aa509fd0ee2c69 (MD5)
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O sensoriamento remoto permite estudar os fenômenos da superfície terrestre em diversas escalas espaciais e temporais. A constante transformação dos ecossistemas, de forma abrupta ou contínua e de origem humana ou natural, cria a necessidade do desenvolvimento de técnicas que tenham a capacidade de detectar essas mudanças, identificar suas causas e monitorar o processo. Este trabalho se volta para as análises de séries temporais contínuas, que podem ser usadas no monitoramento de ecossistemas já que a continuidade dos dados permite traçar um perfil para o comportamento sazonal de cada fitofisionomia. O trabalho tem como objetivo desenvolver uma metodologia para identificação de padrões sazonais e classificação de alvos do uso do território e de áreas alagadas da Ilha do Bananal, estado do Tocantins, a partir do comportamento fenológico identificado nas séries temporais contínuas do sensor MODIS. A partir de então, o desenvolvimento da metodologia foi fracionado em quatro fases principais agrupando etapas temáticas sequenciais de acordo com seu escopo conceitual da seguinte forma: 1) Aquisição de dados do sensor MODIS; 2) Tratamento de Ruído; 3) Construção do Hipercubo Espectro-Temporal; e 4) Classificação e Teste de Acurácia. Para validação da classificação foram utilizados ponto de controle interpretados visualmente com base em imagens Landsat, a partir dos quais se calculou o índice Kappa. O filtro de mediana demonstrou a capacidade de eliminar picos ao mesmo tempo em que preserva os dados. Foi realizada uma classificação para áreas alagáveis – cujo Kappa foi de 0,8 – e uma para o uso da terra – cujo 2 Kappa foi de 0,648. A partir dos mapeamentos foi possível extrair informações sobre os ciclos de inundação e evolução da paisagem na região da Ilha do Bananal.
Remote sensing allows us to study the phenomena of the earth's surface at various spatial and temporal scales. The constant transformation of ecosystems, abruptly or continuously and human induced or from natural origin, creates the need to develop techniques that are able to detect these changes, identify their causes, and monitor the process. This paper turns to the analysis of continuous time series, which can be used in the monitoring of ecosystems, given that the continuity of data allows tracing a profile for the seasonal behavior of each phytphysiognomy. This study’s objective is the development a methodology to identify seasonal patterns and classify land use targets and flooded areas in the Bananal Island, Tocantins state, from the phonologic behavior identified on continuous time series form NODIS sensor. From then on, the methodology development was fractioned in four main stages grouped in thematic sequential steps according to its conceptual scope as follows: 1) Acquisition of MODIS sensor data; 2) Noise Reduction; 3) Construction of the SpectralTemporal Hypercube; and 4) Classification and Accuracy Test. For the classification’s validation it was employed control point visually interpreted based on Landsat images, from which the kappa index was calculated. The median filter demonstrated the capacity of eliminating peaks while preserving the data. It was performed one classification for flooded areas – whose kappa was 0,8 – and one for land use – whose kappa was 0,648. From the mappings, it was possible to extract information regarding the flooding cycles and the landscape development for the Bananal island region.
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Moraes, Maria Valdirene Araújo Rocha. "Morfologia e Sedimentologia do Litoral da Plataforma Continental Interna do Município de Acaraú – Ceará – Brasil." Universidade Federal de Pernambuco, 2012. https://repositorio.ufpe.br/handle/123456789/12135.
Анотація:
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CAPES
O presente trabalho apresenta os resultados dos estudos da morfologia e sedimentologia da região costeira e da plataforma continental interna do município de Acaraú – Ceará e a análise multitemporal da linha de costa. Os objetivos focaram o estudo morfológico e sedimentológico no litoral leste, onde foram realizados 6 (seis) perfis topográficos e coletados sedimentos nos três segmentos praiais característicos da área (pós-praia, estirâncio e planície de maré); a análise sedimentológica da plataforma continental interna e o monitoramento multitemporal da linha de costa por meio de imagens de satélites Landsat 5 TM, dos últimos 21 anos. O método utilizado neste trabalho englobou pesquisas bibliográfica e geocartográfica, levantamento de campo (coleta de amostras, perfis praiais e caracterização ambiental) e análises granulométricas. A análise multitemporal se deu através de técnicas de Processamento Digital de Imagens (PDI). Utilizando os produtos digitais das imagens foi possível a identificação e caracterização dos principais elementos da paisagem. Essa identificação foi possível através do estudo das características espectrais dos resultados desses processamentos digitais. De acordo com os resultados obtidos para a morfologia praial, ocorreu erosão nos perfis 1, 2 e 3, enquanto que ocorreu deposição nos perfis 4, 5 e 6. Quanto às características sedimentológicas, verificou-se diferenciação entre os perfis monitorados. Nos perfis 1 e 2 observou-se uma região lamosa, enquanto que nos perfis 3, 4, 5 e 6 caracterizou-se como arenosa. O modelo batimétrico apresentou uma morfologia de fundo homogênea da isóbata de 0 a 12m. A partir desta isóbata o relevo apresentou-se com declividade suave e com poucas irregularidades, como canais de maré e colinas. O resultado da analise granulométrica da plataforma continental mostrou a predominância de areia bioclástica, caracterizada por apresentar de 70% a 100% de areia e entre 70% a 100% de CaCO3. Dos produtos (mapas) multitemporais da linha de costa pode-se constatar variações dos processos erosivos e construtivos intercaladas na escala do tempo, sendo o litoral leste, a região mais impactada pelos agentes modeladores das feições litorâneas.
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Makkeasorn, Ammarin. "MULTISENSOR FUSION REMOTE SENSING TECHNOLOGY FOR ASSESSING MULTITEMPORAL RESPONSES IN ECOHYDROLOGICAL SYSTEMS." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4068.
Анотація:
Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing–based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Environmental Engineering PhD
43
LEITE, PAULA BEATRIZ CERQUEIRA. "CROP TYPE IDENTIFICATION BASED ON HIDDEN MARKOV MODELS USING MULTITEMPORAL IMAGE SEQUENCES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12960@1.
Анотація:
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOEsta dissertação propõe uma metodologia baseada em Modelos de Markov Ocultos (Hidden Markov Models - HMM) para a classificação de culturas agrícolas, explorando informações de seqüências temporais de imagens dos sensores TM e ETM+/Landsat. O método reconhece os diferentes tipos de culturas agrícolas analisando os perfis espectrais em uma seqüência temporal de imagens de satélite de média resolução espacial ( aproximadamente 30m). Nesta abordagem, o comportamento temporal de cada classe de cultura é modelado por um HMM específico. A classificação é feita segmento-a-segmento, descritos por um vetor de atributos calculado como as médias espectrais dos pixels contidos no segmento em cada banda da imagem. Os vetores de atributos do segmento em cada imagem da seqüência de imagens são subseqüentemente submetidos aos HMMs de cada classe de cultura. O segmento é então associado à cultura cujo HMM correspondente gera a maior probabilidade de emitir a seqüência de valores espectrais observada. Os experimentos para análise foram conduzidos utilizando-se um conjunto de 12 imagens LANDSAT coregistradas e corrigidas radiometricamente. As imagens cobrem uma área do estado de São Paulo, Brasil, com aproximadamente 124.100ha, entre 2002 e 2004. As seguintes coberturas vegetais foram consideradas: cana de açúcar, soja, milho, pastagem e matagaleria. A avaliação do desempenho do método foi efetuada utilizando-se um conjunto de dados classificado visualmente por dois especialistas e validado por um extenso trabalho de campo. O desempenho do método de classificação multitemporal proposto foi comparado com o de um classificador monotemporal de máxima verossimilhança, e os resultados mostraram a superioridade notável do método baseado em HMM, o qual alcançou uma acurácia média de nada menos que 91% na identificação do tipo correto de cultura agrícola, para seqüências de dados contendo apenas uma única classe de cultura.
This work proposes a Hidden Markov Model (HMM)-based methodology to classify agricultural crops, exploring information of temporal image sequences from TM and ETM+/Landsat sensors. HMMs are used to relate the varying spectral response along the crop cycle with plant phenology for different crop classes. The method recognizes different agricultural crops by analyzing their spectral profiles over a temporal sequence of medium resolution satellite images ( approximation 30m). In our approach the temporal behaviour of each crop class is modelled by a specific HMM. A segment- based classification is performed using the average spectral values of the pixels in each image segment across an image sequence, which is subsequently submitted to the HMMs of each crop class. The image segment is assigned to the crop class, whose corresponding HMM delivers the highest probability of emitting the observed sequence of spectral values. Experiments were conducted upon a set of 12 co-registered and radiometrically corrected LANDSAT images. The images cover an area of the State of São Paulo, Brazil with about 124.100ha, between the years 2002 and 2004. The following classes were considered: sugarcane, soybean, corn, pasture and riparian forest. Performance assessment was carried out upon a data set classified visually by two analysts and validated by extensive field work. The performance of the proposed multitemporal classification method was compared to that of a monotemporal maximum likelihood classifier, and the results indicated a remarkable superiority of the HMM-based method, which achieved an average of no less than 91% accuracy in the identification of the correct crop, for sequences of data containing a single crop class.
44
Corsini, Christianne Riquetti. "Análise multitemporal das mudanças de biomassa da vegetação secundária na Amazônia brasileira." Instituto Nacional de Pesquisas Espaciais (INPE), 2018. http://urlib.net/sid.inpe.br/mtc-m21c/2018/05.08.16.56.
Анотація:
A regeneração da vegetação secundária em áreas desmatadas na Amazônia brasileira desempenha um papel importante no balanço de emissões, funcionando como um sumidouro dinâmico de carbono, e mitigando os impactos do desmatamento. O potencial para tal, entretanto, depende das taxas de crescimento desta vegetação no tempo, que vão refletir nos estoques de biomassa acumulado. Como esses estoques variam em relação aos estágios de regeneração e como a dinâmica da vegetação influencia nos padrões de cresciment, ainda é pouco compreendida. Para entender os padrões de acumulo de biomassa na vegetação secundária nos diferentes estágios de regeneração, foram utilizadas trajetórias de cobertura da terra com base nas classes pasto sujo, regeneração com pasto e vegetação secundária do sistema TerraClass e mapas multi-temporais de biomassa acima do solo (BAS) estimados a partir de dados de RADAR do PALSAR-ALOS. A partir destas informações, foi quantificado o crescimento da vegetação secundária em diferentes estágios de regeneração na Amazônia brasileira para os anos de 2007, 2008, 2009 e 2010. Como o crescimento da vegetação secundária é influenciada pelas condições ambientais, também foi testado como os estoques de biomassa variaram em função de diferentes intensidades de máximo déficit hídrico acumulado (MCWD) e com ocorrência de fogo. Os resultados mostraram que a combinação do modelo de trajetórias de classes de cobertura da terra com os mapas de biomassa foi consistente, uma vez que o padrão de BAS foi crescente da trajetória representaviva do início da regeneração (Tr1) até a trajetória de regeneração mais avançada (Tr7). A análise das mudanças inter-anuais de BAS dentro de cada trajetória mostrou o incremento anual potencial no processo de regeneração do pasto sujo até a regeneração com pasto, ao passo que as trajetórias envolvendo a classe vegetação secundária apresentaram redução de biomassa em algum dos períodos analisados. A estratificação da BAS nessas trajetórias em função dos níveis de MCWD e ocorrência de fogo revelou a magnitude do impacto deles sobre o acúmulo de biomassa, sugerindo que a umidade tem papel fundamental no processo de crescimento, ao passo que a ocorrência de fogo é o principal agente redutor do incremento. A análise das mudanças inter-anuais de BAS por classe de distúrbio revelou que o deficit hídrico foi o principal causador de perdas de biomassa nas vegetações mais avançadas, enquanto o fogo foi mais danoso nas vegetações secundárias mais jovens. No balanço final do carbono, as trajetórias envolvendo pasto sujo e regeneração com pasto não apresentaram influência significativa, funcionando apenas como parâmetro de crescimento. A vegetação secundária, por outro lado, mostrou grande potencial de impacto nas emissões, respondendo por mais de 80% dos valores encontrados no balanço final do carbono.The regrowth of secondary vegetation on deforested areas in the Brazilian Amazon plays an important role in the emissions balance, functioning as a dynamic carbon sink, and mitigating the impacts of deforestation. The potential for this, however, depends on the rates of vegetation growth over time, which will reflect in the accumulated biomass stocks. How these stocks vary in relation to the stages of regeneration and how the dynamics of vegetation influences growth patterns is still poorly understood. For understanding the patterns of carbon accumulation in secondary vegetation across different stages of regeneration, land cover trajectories based on the classes 'dirty pasture', 'regeneration with pasture' and 'secondary vegetation' of the TerraClass system and multi-temporal maps of above-ground biomass (BAS) estimated from the RADAR PALSAR-ALOS data were used. Based on these data, vegetation growth at different stages of regeneration in the Brazilian Amazon for the years 2007, 2008, 2009 and 2010 was quantified. As secondary vegetation growth is influenced by environmental conditions, it was also tested how the biomass stocks varied according to different intensities of maximum accumulated water deficit (MCWD) and of the occurrence of fire. The results showed that the combination of the trajectory model of land cover classes with the biomass maps was consistent. The estimated BAS increased from the trajectory representing the beginning of regeneration (Tr1) to the trajectory representing the most advanced regeneration stage (Tr7). The analysis of the inter-annual BAS changes within each trajectory showed the annual BAS increase from 'dirty pasture' to 'regeneration with pasture', while the trajectories involving the 'secondary vegetation' class presented a reduction in biomass in any of the periods analyzed. The stratification of BAS in these trajectories as a function of disturbance classes revealed the magnitude of their impact on the accumulation of biomass. The results suggested that water deficit plays a fundamental role in the growth process, whereas the occurrence of fire is the main agent constraining biomass increase throught time. The analysis of the inter-annual changes of BAS by classes of disturbance revealed that the water deficit was the main cause of biomass loss in the most advanced stages of vegetation regeneration, while fire was more damaging in the younger secondary vegetation. In the final carbon balance, the trajectories involving 'shurubby pasture' and 'regeneration with pasture' did not present significant influence, functioning only as a growth parameter. Secondary vegetation, on the other hand, showed great potential for impact on emissions, accounting for more than 80% of the values found in the final carbon balance.
45
Cai, Zipan. "Multitemporal Satellite Data for Monitoring Urbanization in Nanjing from 2001 to 2016." Thesis, KTH, Geoinformatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214036.
Анотація:
Along with the increasing rate of urbanization takes place in the world, the population keeps shifting from rural to urban areas. China, as the country of the largest population, has the highest urban population growth in Asia, as well as the world. However, the urbanization in China, in turn, is leading to a lot of social issues which reshape the living environment and cultural fabric. A variety of these kinds of social issues emphasize the challenges regarding a healthy and sustainable urban growth particularly in the reasonable planning of urban land use and land cover features. Therefore, it is significant to establish a set of comprehensive urban sustainable development strategies to avoid detours in the urbanization process. Nowadays, faced with such as a series of the social phenomenon, the spatial and temporal technological means including Remote Sensing and Geographic Information System (GIS) can be used to help the city decision maker to make the right choices. The knowledge of land use and land cover changes in the rural and urban area assists in identifying urban growth rate and trend in both qualitative and quantitatively ways, which provides more basis for planning and designing a city in a more scientific and environmentally friendly way. This paper focuses on the urban sprawl analysis in Nanjing, Jiangsu, China that being analyzed by urban growth pattern monitoring during a study period. From 2001 to 2016, Nanjing Municipality has experienced a substantial increase in the urban area because of the growing population. In this paper, one optimal supervised classification with high accuracy which is Support Vector Machine (SVM) classifier was used to extract thematic features from multitemporal satellite data including Landsat 7 ETM+, Landsat 8, and Sentinel-2A MSI. It was interpreted to identify the existence of urban sprawl pattern based on the land use and land cover features in 2001, 2006, 2011, and 2016. Two different types of change detection analysis including post-classification comparison and change vector analysis (CVA) were performed to explore the detailed extent information of urban growth within the study region. A comparison study on these two change detection analysis methods was carried out by accuracy assessment. Based on the exploration of the change detection analysis combined with the current urban development actuality, some constructive recommendations and future research directions were given at last. By implementing the proposed methods, the urban land use and land cover changes were successfully captured. The results show there is a notable change in the urban or built-up land feature. Also, the urban area is increased by 610.98 km2 while the agricultural land area is decreased by 766.96 km2, which proved a land conversion among these land cover features in the study period. The urban area keeps growing in each particular study period while the growth rate value has a decreasing trend in the period of 2001 to 2016. Besides, both change detection techniques obtained the similar result of the distribution of urban expansion in the study area. According to the result images from two change detection methods, the expanded urban or built-up land in Nanjing distributes mainly in the surrounding area of the central city area, both side of Yangtze River, and Southwest area. The results of change detection accuracy assessment indicated the post-classification comparison has a higher overall accuracy 86.11% and a higher Kappa Coefficient 0.72 than CVA. The overall accuracy and Kappa Coefficient for CVA is 75.43% and 0.51 respectively. These results proved the strength of agreement between predicted and truth data is at ‘good’ level for post-classification comparison and ‘moderate’ for CVA. Also, the results further confirmed the expectation from previous studies that the empirical threshold determination of CVA always leads to relatively poor change detection accuracy. In general, the two change detection techniques are found to be effective and efficient in monitoring surface changes in the different class of land cover features within the study period. Nevertheless, they have their advantages and disadvantages on processing change detection analysis particularly for the topic of urban expansion.
46
MOREIRA, Elvis Bergue Mariz. "Variação espacial e multitemporal das temperaturas da superfície na cidade do Recife." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/6252.
Анотація:
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O município do Recife, nos últimos anos 30 anos, teve seu crescimento urbano intensificado, o que resulta substituição de áreas verdes por uma zona urbana edificada, impermeabilizando o solo e contribuindo para o aumento das temperaturas. Portanto mudanças locais causadas por edificações e ocupações inadequadas provocaram alterações no conforto urbano ambiental. O trabalho proposto analisa através de imagens multiespectrais do Landsat-5 TM, a variação espacial e multitemporal das temperaturas na cidade do Recife. Para tanto foram estimados índices de vegetação (IVAS), albedo, emissividade e finalmente temperatura. Foram utilizadas duas imagens referentes às datas 10 de junho de 1984 e 29 de agosto de 2007. O Índice de Vegetação Ajustado ao Solo (IVAS) apresentou valores médios de 0,164 em 1984 e 0,129 para o ano de 2007. De acordo com os resultados houve uma diminuição de 0,035 concernentes a cobertura vegetal, corroborando desta forma com a intensificação dos espaços urbanos ocorridos nos últimos anos. O valor máximo encontrado para o albedo da superfície foi de 0,25 para o ano de 1984 e 0,33 para o ano de 2007, ano de 1984 apresentou os menores valores. A temperatura da superfície terrestre estimada foi maior para o ano de 2007 sendo 27ºC seu valor médio e menor para o ano de 1984 com média de 22,2ºC. Os maiores valores de temperatura da superfície em todas as imagens estudadas encontram-se no setor sul onde estar localizado o bairro de Boa Viagem e no centro da Cidade, sofrendo uma variação de aproximadamente 5ºC. Em 1984 a temperatura apresentou-se mais concentrada entre 19ºC e 24ºC e em 2007 ocorreu uma maior variação ficando entre 22ºC e 32ºC aproximadamente
47
Zanetti, Massimo. "Advanced methods for the analysis of multispectral and multitemporal remote sensing images." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368741.
Анотація:
The increasing availability of new generation remote sensing satellite multispectral images provides an unprecedented source of information for Earth observation and monitoring. Multispectral images can be now collected at high resolution covering (almost) all land surfaces with extremely short revisit time (up to a few days), making it possible the mapping of global changes. Extracting useful information from such huge amount of data requires a systematic use of automatic techiques in almost all applicative contexts. In some cases, the strict application requirements force the pratictioner to develop strongly data-driven approaches in the development of the processing chain. As a consequence, the exact relationship between the theoretical models adopted and the physical meaning of the solutions is sometimes hidden in the data analysis techniques, or not clear at all. Altough this is not a limitation for the success of the application itself, it makes however dicult to transfer the knowledge learned from one specic problem to another. In this thesis we mainly focus on this aspect and we propose a general mathematical framework for the representation and analysis of multispectral images. The proposed models are then used in the applicative context of change detection. Here, the generality of the proposed models allows us to both: (1) provide a mathematical explanation of already existing methodologies for change detection, and (2) extend them to more general cases for addressing problems of increasing complexity. Typical spatial/spectral properties of last generation multispectral images emphasize the need of having more exible models to image representation. In fact, classical methods to change detection that have worked well on previous generations of multispectral images provide sub-optimal results due to their poor capability of modeling all the complex spectral/spatial detail available in last generation products. The theoretical models presented in this thesis are aimed at giving more degrees of freedom in the representation of the images. The eectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving both synthetic datasets and real multispectral images. Here, the improved flexibility of the models adopted allows for a better representation of the data and is always followed by a substantial improvement of the change detection performance.
48
Zanetti, Massimo. "Advanced methods for the analysis of multispectral and multitemporal remote sensing images." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2041/1/zanetti_phd-thesis.pdf.
Анотація:
The increasing availability of new generation remote sensing satellite multispectral images provides an unprecedented source of information for Earth observation and monitoring. Multispectral images can be now collected at high resolution covering (almost) all land surfaces with extremely short revisit time (up to a few days), making it possible the mapping of global changes. Extracting useful information from such huge amount of data requires a systematic use of automatic techiques in almost all applicative contexts. In some cases, the strict application requirements force the pratictioner to develop strongly data-driven approaches in the development of the processing chain. As a consequence, the exact relationship between the theoretical models adopted and the physical meaning of the solutions is sometimes hidden in the data analysis techniques, or not clear at all. Altough this is not a limitation for the success of the application itself, it makes however dicult to transfer the knowledge learned from one specic problem to another. In this thesis we mainly focus on this aspect and we propose a general mathematical framework for the representation and analysis of multispectral images. The proposed models are then used in the applicative context of change detection. Here, the generality of the proposed models allows us to both: (1) provide a mathematical explanation of already existing methodologies for change detection, and (2) extend them to more general cases for addressing problems of increasing complexity. Typical spatial/spectral properties of last generation multispectral images emphasize the need of having more exible models to image representation. In fact, classical methods to change detection that have worked well on previous generations of multispectral images provide sub-optimal results due to their poor capability of modeling all the complex spectral/spatial detail available in last generation products. The theoretical models presented in this thesis are aimed at giving more degrees of freedom in the representation of the images. The eectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving both synthetic datasets and real multispectral images. Here, the improved flexibility of the models adopted allows for a better representation of the data and is always followed by a substantial improvement of the change detection performance.
49
Zhao, Weiying. "Multitemporal SAR images denoising and change detection : applications to Sentinel-1 data." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT003/document.
Анотація:
Le bruit de chatoiement (speckle) lié aux systèmes d'imagerie cohérente a des conséquences sur l'analyse et l'interprétation des images radar à synthèse d'ouverture (RSO). Pour corriger ce défaut, nous profitons de séries temporelles d'images RSO bien recalées. Nous améliorons le filtre adaptatif temporel non-local à l'aide de méthodes performantes de débruitage adaptatif et proposons un filtrage temporel adaptatif basé sur les patchs. Pour réduire le biais du débruitage, nous proposons une méthode originale, rapide et efficace de débruitage multitemporel. L'idée principale de l'approche proposée est d'utiliser l'image dite "de ratio", donnée par le rapport entre l'image et la moyenne temporelle de la pile. Cette image de ratio est plus facile à débruiter qu'une image isolée en raison de sa meilleure stationnarité. Par ailleurs, les structures fines stables dans le temps sont bien préservées grâce au moyennage multitemporel. Disposant d'images débruitées, nous proposons ensuite d'utiliser la méthode du rapport de vraisemblance généralisé simplifié pour détecter les zones de changement ainsi que l'amplitude des changements et les instants de changements intéressants dans de longues séries d'images correctement recalées. En utilisant le partitionnement spectral, on applique le rapport de vraisemblance généralisé simplifié pour caractériser les changements des séries temporelles. Nous visualisons les résultats de détection en utilisant l'échelle de couleur 'jet' et une colorisation HSV. Ces méthodes ont été appliquées avec succès pour étudier des zones cultivées, des zones urbaines, des régions portuaires et des changements dus à des inondationsThe inherent speckle which is attached to any coherent imaging system affects the analysis and interpretation of synthetic aperture radar (SAR) images. To take advantage of well-registered multi-temporal SAR images, we improve the adaptive nonlocal temporal filter with state-of-the-art adaptive denoising methods and propose a patch based adaptive temporal filter. To address the bias problem of the denoising results, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well-preserved thanks to the multi-temporal mean. Without reference image, we propose to use a patch-based auto-covariance residual evaluation method to examine the residual image and look for possible remaining structural contents. With speckle reduction images, we propose to use simplified generalized likelihood ratio method to detect the change area, change magnitude and change times in long series of well-registered images. Based on spectral clustering, we apply the simplified generalized likelihood ratio to detect the time series change types. Then, jet colormap and HSV colorization may be used to vividly visualize the detection results. These methods have been successfully applied to monitor farmland area, urban area, harbor region, and flooding area changes
50
Zhao, Weiying. "Multitemporal SAR images denoising and change detection : applications to Sentinel-1 data." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT003.
Анотація:
Le bruit de chatoiement (speckle) lié aux systèmes d'imagerie cohérente a des conséquences sur l'analyse et l'interprétation des images radar à synthèse d'ouverture (RSO). Pour corriger ce défaut, nous profitons de séries temporelles d'images RSO bien recalées. Nous améliorons le filtre adaptatif temporel non-local à l'aide de méthodes performantes de débruitage adaptatif et proposons un filtrage temporel adaptatif basé sur les patchs. Pour réduire le biais du débruitage, nous proposons une méthode originale, rapide et efficace de débruitage multitemporel. L'idée principale de l'approche proposée est d'utiliser l'image dite "de ratio", donnée par le rapport entre l'image et la moyenne temporelle de la pile. Cette image de ratio est plus facile à débruiter qu'une image isolée en raison de sa meilleure stationnarité. Par ailleurs, les structures fines stables dans le temps sont bien préservées grâce au moyennage multitemporel. Disposant d'images débruitées, nous proposons ensuite d'utiliser la méthode du rapport de vraisemblance généralisé simplifié pour détecter les zones de changement ainsi que l'amplitude des changements et les instants de changements intéressants dans de longues séries d'images correctement recalées. En utilisant le partitionnement spectral, on applique le rapport de vraisemblance généralisé simplifié pour caractériser les changements des séries temporelles. Nous visualisons les résultats de détection en utilisant l'échelle de couleur 'jet' et une colorisation HSV. Ces méthodes ont été appliquées avec succès pour étudier des zones cultivées, des zones urbaines, des régions portuaires et des changements dus à des inondationsThe inherent speckle which is attached to any coherent imaging system affects the analysis and interpretation of synthetic aperture radar (SAR) images. To take advantage of well-registered multi-temporal SAR images, we improve the adaptive nonlocal temporal filter with state-of-the-art adaptive denoising methods and propose a patch based adaptive temporal filter. To address the bias problem of the denoising results, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well-preserved thanks to the multi-temporal mean. Without reference image, we propose to use a patch-based auto-covariance residual evaluation method to examine the residual image and look for possible remaining structural contents. With speckle reduction images, we propose to use simplified generalized likelihood ratio method to detect the change area, change magnitude and change times in long series of well-registered images. Based on spectral clustering, we apply the simplified generalized likelihood ratio to detect the time series change types. Then, jet colormap and HSV colorization may be used to vividly visualize the detection results. These methods have been successfully applied to monitor farmland area, urban area, harbor region, and flooding area changes