Готові списки джерел за темами / Multitemporel

Добірка наукової літератури з теми "Multitemporel"

Автор: Grafiati
Опубліковано: 7 липня 2024

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Статті в журналах з теми "Multitemporel":

1

Octariady, J., A. Hikmat, E. Widyaningrum, R. Mayasari, and M. K. Fajari. "VERTICAL ACCURACY COMPARISON OF DIGITAL ELEVATION MODEL FROM LIDAR AND MULTITEMPORAL SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 419–23. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-419-2017.

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Digital elevation model serves to illustrate the appearance of the earth's surface. DEM can be produced from a wide variety of data sources including from radar data, LiDAR data, and stereo satellite imagery. Making the LiDAR DEM conducted using point cloud data from LiDAR sensor. Making a DEM from stereo satellite imagery can be done using same temporal or multitemporal stereo satellite imagery. How much the accuracy of DEM generated from multitemporal stereo stellite imagery and LiDAR data is not known with certainty. The study was conducted using LiDAR DEM data and multitemporal stereo satellite imagery DEM. Multitemporal stereo satellite imagery generated semi-automatically by using 3 scene stereo satellite imagery with acquisition 2013–2014. The high value given each of DEM serve as the basis for calculating high accuracy DEM respectively. The results showed the high value differences in the fraction of the meter between LiDAR DEM and multitemporal stereo satellite imagery DEM.
2

Huang, Liang, Qiuzhi Peng, and Xueqin Yu. "Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering." Journal of Spectroscopy 2020 (March 23, 2020): 1–9. http://dx.doi.org/10.1155/2020/2725186.

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In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.
3

Zhang, Xiaokang, Wenzhong Shi, Zhiyong Lv, and Feifei Peng. "Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model." Remote Sensing 11, no. 23 (November 26, 2019): 2787. http://dx.doi.org/10.3390/rs11232787.

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This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.
4

Fosbury, Adam M. "Estimation with Multitemporal Measurements." Journal of Guidance, Control, and Dynamics 33, no. 5 (September 2010): 1518–26. http://dx.doi.org/10.2514/1.47984.

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5

Zhu, Wei, Qian Du, and James E. Fowler. "Multitemporal Hyperspectral Image Compression." IEEE Geoscience and Remote Sensing Letters 8, no. 3 (May 2011): 416–20. http://dx.doi.org/10.1109/lgrs.2010.2081661.

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6

Oliveira Soares, Eduardo. "A MULTITEMPORAL VILA ITORORÓ." PIXO - Revista de Arquitetura, Cidade e Contemporaneidade 7, no. 24 (March 23, 2023): 140–51. http://dx.doi.org/10.15210/pixo.v7i24.3220.

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Vila Itororó foi inaugurada em 1922 e está localizada na cidade de São Paulo. O conjunto arquitetônico inicialmente era formado por um palacete, casas de aluguel e uma piscina. Desde 2013 abriga um Centro Cultural. A configuração arquitetônica atual revela a variedade de usos e de públicos ao longo do tempo; as mudanças urbanas da cidade e do bairro; as oscilações entre os perfis dos moradores; e as abordagens sobre como lidar com o patrimônio das cidades. Esses fatores ajudaram a forjar, ao longo de um século, um conjunto arquitetônico que mescla edificações já restauradas, obras em andamento e estruturas aparentemente abandonadas. O artigo apresenta uma narrativa textual e fotográfica a partir de uma visita realizada em 2022, registrando atributos e percepções de um patrimônio marcado pela atuação do tempo.
7

Shu, Chang, and Lihui Sun. "Automatic target recognition method for multitemporal remote sensing image." Open Physics 18, no. 1 (June 5, 2020): 170–81. http://dx.doi.org/10.1515/phys-2020-0015.

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AbstractThe traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.
8

Gutierrez, Laura, Elías Haro, and Natalia Díaz. "Multitemporal Analysis of Potential Geographic Distribution of Lama Guanicoe." Revista Ciencia y Tecnología 20, no. 1 (March 8, 2024): 89–100. http://dx.doi.org/10.17268/rev.cyt.2024.01.07.

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The purpose of this research is focused on modeling the geographic distribution of Lama guanicoe in South America in two time periods, 2021 and 2070, using MaxEnt software to correlate the bioclimatic variables and calculate the change in the distribution area. Then, with this information, a comparative map of the current and future areas was made in QGIS. As conclusions we can see that in the region of Peru the change of distribution area is reduced, which is observable in the majority of the nations where the guanaco (Lama guanicoe) lives, which is currently considered Endangered, so according to our model it predicts that it will reduce the distribution area by 20%, and the temperature variables have a negative correlation with the area, which indicates that climate change will have a relationship with the Lama guanicoe. This information is necessary for all countries to take action in the conservation of Lama guanicoe by adopting strategies to reduce and prevent climate change, generating and updating their conservation plans.
9

Ilteralp, Melike, Sema Ariman, and Erchan Aptoula. "A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images." Remote Sensing 14, no. 1 (December 22, 2021): 18. http://dx.doi.org/10.3390/rs14010018.

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This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.
10

Cheng, Xinglu, Yonghua Sun, Wangkuan Zhang, Yihan Wang, Xuyue Cao, and Yanzhao Wang. "Application of Deep Learning in Multitemporal Remote Sensing Image Classification." Remote Sensing 15, no. 15 (August 3, 2023): 3859. http://dx.doi.org/10.3390/rs15153859.

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The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image classification can extract richer spatiotemporal features. However, this also presents the challenge of mining massive data features. In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. The combination of remote sensing classification and deep learning has become a trend and has developed rapidly in recent years. However, there is a lack of summary and discussion on the research status and trends in multitemporal images. This review retrieved and screened 170 papers and proposed a research framework for this field. It includes retrieval statistics from existing research, preparation of multitemporal datasets, sample acquisition, an overview of typical models, and a discussion of application status. Finally, this paper discusses current problems and puts forward prospects for the future from three directions: adaptability between deep learning models and multitemporal classification, prospects for high-resolution image applications, and large-scale monitoring and model generalization. The aim is to help readers quickly understand the research process and application status of this field.
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Статті в журналах Дисертації Книги

Дисертації з теми "Multitemporel":

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.

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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 segmentation
This 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.

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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.

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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 chimiques
Anthropogenic 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.

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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.

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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.

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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-X
A 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.

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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.

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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.

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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

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Vicente-Guijalba, Fernando. "Teledetección Multitemporal mediante Dinámica de Sistemas." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/57626.

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Книги з теми "Multitemporel":

1

Ban, Yifang, ed. Multitemporal Remote Sensing. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5.

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2

Franco, Rodolfo. Análisis satelital multitemporal de los bosques del Carare-Opón. Bogotá, Colombia: Universidad Distrital Francisco José de Caldas, Centro de Investigaciones y Desarrollo Científico, 2004.

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3

Howell, Signe, and Aud Talle. Returns to the field: Multitemporal research and contemporary anthropology. Bloomington: Indiana University Press, 2012.

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4

Ager, Alan A. Characterizing meadow vegetation with multitemporal landsat thematic mapper remote sensing. Portland, OR]: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2004.

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5

United States. National Aeronautics and Space Administration., ed. Relating multitemporal meteorological satellite date to climatological data for Africa: Semi-annual report, August 1986 - March 1987. [Washington, D.C: National Aeronautics and Space Administration, 1987.

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United States. National Aeronautics and Space Administration, ed. Relating multitemporal meteorological satellite date to climatological data for Africa: Semi-annual report, August 1986 - March 1987. [Washington, D.C: National Aeronautics and Space Administration, 1987.

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7

Heering, Judith M. Multitemporale Luftbildauswertung zur Dokumentation und Analyse der Entwicklung postindustrieller Vegetation am Beispiel des Industriewaldstandortes Rheinelbe. Bochum: Geographisches Institut der Universität Bochum, 2008.

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8

Heering, Judith M. Multitemporale Luftbildauswertung zur Dokumentation und Analyse der Entwicklung postindustrieller Vegetation am Beispiel des Industriewaldstandortes Rheinelbe. Bochum: Geographisches Institut der Universität Bochum, 2008.

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9

Peng, Shikui. On the combination of multitemporal satellite and field data for forest inventories =: Moniaikaisen satelliitti- ja maastoaineiston yhteiskäyttö metsien inventoinnissa. Helsinki: Suomen Metsätieteellinen Seura, 1987.

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10

Ecuador. Subsecretaría de Recursos Pesqueros. Estudio multitemporal de manglares, camaroneras y areas salinas de la costa ecuatoriana, mediante información de sensores remotos (1969-1984): Memoria técnica. Quito, Ecuador: La Subsecretaría, 1986.

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Частини книг з теми "Multitemporel":

1

Ban, Yifang. "Multitemporal Remote Sensing: Current Status, Trends and Challenges." In Multitemporal Remote Sensing, 1–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_1.

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2

Rodrigues, Arlete, André R. S. Marcal, and Mário Cunha. "PhenoSat – A Tool for Remote Sensing Based Analysis of Vegetation Dynamics." In Multitemporal Remote Sensing, 195–215. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_10.

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3

Verger, Aleixandre, Sivasathivel Kandasamy, and Frédéric Baret. "Temporal Techniques in Remote Sensing of Global Vegetation." In Multitemporal Remote Sensing, 217–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_11.

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4

Gumbricht, Thomas. "Soil Moisture Dynamics Estimated from MODIS Time Series Images." In Multitemporal Remote Sensing, 233–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_12.

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5

He, Tao, and Shunlin Liang. "Temporal Analysis of Remotely Sensed Land Surface Shortwave Albedo." In Multitemporal Remote Sensing, 255–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_13.

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6

Menenti, Massimo, H. R. Ghafarian Malamiri, Haolu Shang, Silvia M. Alfieri, Carmine Maffei, and Li Jia. "Observing the Response of Terrestrial Vegetation to Climate Variability Across a Range of Time Scales by Time Series Analysis of Land Surface Temperature." In Multitemporal Remote Sensing, 277–315. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_14.

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7

McNairn, Heather, and Jiali Shang. "A Review of Multitemporal Synthetic Aperture Radar (SAR) for Crop Monitoring." In Multitemporal Remote Sensing, 317–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_15.

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8

Brown, Nicholas D. A., Trisalyn Nelson, Michael A. Wulder, Nicholas C. Coops, Thomas Hilker, Christopher W. Bater, Rachel Gaulton, and Gordon B. Stenhouse. "An Approach for Determining Relationships Between Disturbance and Habitat Selection Using Bi-weekly Synthetic Images and Telemetry Data." In Multitemporal Remote Sensing, 341–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_16.

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9

Wang, Yeqiao, Shuhua Qi, and Jian Xu. "Multitemporal Remote Sensing for Inland Water Bodies and Wetland Monitoring." In Multitemporal Remote Sensing, 357–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_17.

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10

Cao, Xin, Jun Chen, Anping Liao, Lijun Chen, and Jin Chen. "Global Land Surface Water Mapping and Analysis at 30 m Spatial Resolution for Years 2000 and 2010." In Multitemporal Remote Sensing, 373–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_18.

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Тези доповідей конференцій з теми "Multitemporel":

1

Jin, Huiran, Peijun Li, and Wenjie Fan. "Land Cover Classification using Multitemporal CHRIS/PROBA Images and Multitemporal Texture." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779829.

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2

Dagurov, P. N., A. V. Dmitriev, T. N. Chimitdorzhiev, A. K. Baltukhaev, and I. I. Kirbizhekova. "Backscatter analysis of C-band radar signals using Sentinel-1 multitemporal data (test site near lake Baikal)." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.71.20.007.

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The results of the analysis of multitemporal data of the Sentinel-1 radar for the test site near Lake Baikal are presented. The analysis of the seasonal dependences of backscattering from the soil is carried out. The connection between the signal level and the processes of freezing and thawing and temperature values has been established.
3

Sigurdsson, Jakob, Magnus O. Ulfarsson, and Johannes R. Sveinsson. "Fast multitemporal hyperspectral unmixing." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8126933.

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4

Schröder, Daniel, Katharina Anders, Lukas Winiwarter, and Daniel Wujanz. "Permanent terrestrial LiDAR monitoring in mining, natural hazard prevention and infrastructure protection – Chances, risks, and challenges: A case study of a rockfall in Tyrol, Austria." In 5th Joint International Symposium on Deformation Monitoring. Valencia: Editorial de la Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/jisdm2022.2022.13649.

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The objective of this work is the development of an integrated monitoring service for the identification and evaluation of ground surface and slope movements in the context of coal mining, the prevention of natural hazards and protection of infrastructure. The focus is set on the integration of a long-range terrestrial laser scanner into a continuous monitoring system from an engineering geodetic point of view. In the Vals valley in Tyrol, a permanently installed laser scanner was successfully operated via a web portal to monitor surface processes in the area of rockfall debris on a high-mountain slope in the summers of 2020 and 2021. This paper describes the practical benefits of this permanent laser scanning installation. In addition to the potentials of automatic data acquisition, possibilities for multitemporal analysis with respect to spatio-temporally variable changes are presented, using advanced 3D change detection with Kalman filtering. The level of detection for deformation analyses therein depends on the quality of the georeferencing of the sensor and the noise within the measured point cloud. We identify and discuss temporally variable artifacts within the data based on different methods of georeferencing. Finally, we apply our change detection method on these multitemporal data to extract specific information regarding the observed geomorphologic processes.
5

Amitrano, Donato, Francesca Cecinati, Gerardo Di Martino, Antonio Iodice, Daniele Riccio, and Giuseppe Ruello. "Sentinel-1 multitemporal SAR products." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326695.

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6

CARRANZA, MARIA LAURA, ALICIA ACOSTA, and CARLO RICOTTA. "MULTITEMPORAL PHENOLOGICAL CLASSIFICATION OF ARGENTINA." In Proceedings of the First International Workshop on Multitemp 2001. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777249_0026.

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7

Coren, Franco. "Multitemporal Lidar Monitoring of Landslides." In 73rd EAGE Conference and Exhibition - Workshops 2011. Netherlands: EAGE Publications BV, 2011. http://dx.doi.org/10.3997/2214-4609.20144694.

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8

Natali, S., A. Beccati, S. D'Elia, M. G. Veratelli, P. Campalani, M. Folegani, and S. Mantovani. "Multitemporal data management and exploitation infrastructure." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005087.

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9

Kiang, Richard K. "Multitemporal multispectral classification of global vegetation." In BiOS '98 International Biomedical Optics Symposium, edited by Carol J. Cogswell, Jose-Angel Conchello, Jeremy M. Lerner, Thomas T. Lu, and Tony Wilson. SPIE, 1998. http://dx.doi.org/10.1117/12.310564.

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10

Oliver, Christopher J., Ian McConnell, and Douglas G. Corr. "Multitemporal change detection for SAR imagery." In Remote Sensing, edited by Francesco Posa. SPIE, 1999. http://dx.doi.org/10.1117/12.373159.

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Звіти організацій з теми "Multitemporel":

1

McNairn, H., D. Wood, and R. J. Brown. Mapping Crop Characteristics Using Multitemporal RADARSAT Imager. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219318.

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2

Ager, Alan A., and Karen E. Owens. Characterizing meadow vegetation with multitemporal Landsat thematic mapper remote sensing. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2004. http://dx.doi.org/10.2737/pnw-rn-544.

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3

McNairn, H., R. J. Brown, and D. Wood. Incidence Angle Considerations for Crop Mapping Using Multitemporal RADARSAT Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219347.

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4

Groeneveld, Davis, and Williams. L51974 Automated Detection of Encroachment Events Using Satellite Remote Sensing. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2002. http://dx.doi.org/10.55274/r0011300.

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As an integral part of the ongoing effort to develop an operational capability of remote sensing based pipeline encroachment monitoring, this investigation focused on the development of automated target detection using synthetic aperture radar (RADARSAT) and optical (QUICKBIRD, EROS) satellite imagery. Specifically, the study aimed at meeting the following objectives: To develop automated target detection algorithms for optical and radar imagery that replicate detection rates obtained through visual image interpretation; To investigate the utility of newly available high-resolution optical satellite imagery for encroachment monitoring; To reduce false alarms through the processing of multitemporal radar images; and To identify and prioritize areas of future research and development required for the operational application of the technology.
5

Puestow. L52194 Detection of Third Party Encroachment Using Satellite Based Remote Sensing Technologies. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2015. http://dx.doi.org/10.55274/r0011045.

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Building on past experience, it was the objective of this investigation to automate the satellite-based detection of encroachment events, to improve target detection and reduce false alarms using radar and optical imagery and to investigate the integration of one-call services into the process flow. Algorithm development for target detection using optical imagery was carried out with the intention to facilitate the future integration of unmanned airborne vehicle (UAV) technology into the process. The capacity of the multitemporal algorithm was extended to enable the detection of area changes in addition to vehicle targets. The integration of existing notification services in the satellite-based approach was examined. A satellite-based encroachment monitoring system is now in place to undertake large-scale field demonstrations over 100 to 200 miles of right-of-way for a period of several months, preceded by a pre-service calibration phase of several weeks to adjust the procedures to local conditions. A constant false alarm rate between 5 and 10% can be achieved after a service period of 8 to 10 months.
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A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed. US Geological Survey, 2011. http://dx.doi.org/10.3133/ofr20111131.

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