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Dissertations / Theses on the topic 'Hyperspectral and multispectral data fusion'
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Vivone, Gemine. "Multispectral and hyperspectral pansharpening." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1604.
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2012-2013Remote sensing consists in measuring some characteristics of an object from a distance. A key example of remote sensing is the Earth observation from sensors mounted on satellites that is a crucial aspect of space programs. The first satellite used for Earth observation was Explorer VII. It has been followed by thousands of satellites, many of which are still working. Due to the availability of a large number of different sensors and the subsequent huge amount of data collected, the idea of obtaining improved products by means of fusion algorithms is becoming more intriguing. Data fusion is often exploited for indicating the process of integrating multiple data and knowledge related to the same real-world scene into a consistent, accurate, and useful representation. This term is very generic and it includes different levels of fusion. This dissertation is focused on the low level data fusion, which consists in combining several sources of raw data. In this field, one of the most relevant scientific application is surely the Pansharpening. Pansharpening refers to the fusion of a panchromatic image (a single band that covers the visible and near infrared spectrum) and a multispectral/hyperspectral image (tens/hundreds bands) acquired on the same area. [edited by author]
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Ahn, Byung Joon. "Design and development of a work-in-progress, low-cost Earth Observation multispectral satellite for use on the International Space Station." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587426345809705.
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Jacq, Kévin. "Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles. High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core High-resolution grain size distribution of sediment core with 2 hyperspectral imaging Study of pansharpening methods applied to hyperspectral images of sediment cores." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAA024.
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L'évolution de l'environnement et le climat sont, actuellement, au centre de toutes les attentions. Les impacts de l'activité des sociétés actuelles et passées sur l'environnement sont notamment questionnés pour mieux anticiper les implications de nos activités sur le futur. Mieux décrire les environnements passés et leurs évolutions sont possibles grâce à l'étude de nombreux enregistreurs naturels (sédiments, spéléothèmes, cernes, coraux). Grâce à eux, il est possible de caractériser des évolutions bio-physico-chimiques à différentes résolutions temporelles et pour différentes périodes. La haute résolution entendue ici comme la résolution su sante pour l'étude de l'environnement en lien avec l'évolution des sociétés constitue le principal verrou de l'étude de ces archives naturelles notamment en raison de la capacité analytique des appareils qui ne peuvent que rarement voir des structures fines inframillimétriques. Ce travail est bâti autour de l'hypothèse que l'utilisation de caméras hyperspectrales (VNIR, SWIR, LIF) couplée à des méthodes statistiques pertinentes doivent permettre d'accéder aux informations spectrales et donc bio-physico-chimiques contenues dans ces archives naturelles à une résolution spatiale de quelques dizaines de micromètres et, donc, de proposer des méthodes pour atteindre la haute résolution temporelle (saisonnière). De plus, a n d'avoir des estimations ables, plusieurs capteurs d'imageries et de spectroscopies linéaires (XRF, TRES) sont utilisés avec leurs propres caractéristiques (résolutions, gammes spectrales, interactions atomiques/moléculaires). Ces méthodes analytiques sont utilisées pour la caractérisation de la surface des carottes sédimentaires. Ces analyses spectrales micrométriques sont mises en correspondance avec des analyses géochimiques millimétriques usuelles. Optimiser la complémentarité de toutes ces données, implique de développer des méthodes permettant de dépasser la difficulté inhérente au couplage de données considérées par essence dissimilaire (résolutions, décalages spatiaux, non-recouvrement spectral). Ainsi, quatre méthodes ont été développées. La première consiste à associer les méthodes hyperspectrales et usuelles pour la création de modèles prédictifs quantitatifs. La seconde permet le recalage spatial des différentes images hyperspectrales à la plus basse des résolutions. La troisième s'intéresse à la fusion de ces dernières à la plus haute des résolutions. Enfin, la dernière s'intéresse aux dépôts présents dans les sédiments (lamines, crues, tephras) pour ajouter une dimension temporelle à nos études. Grâce à l'ensemble de ces informations et méthodes, des modèles prédictifs multivariés ont été estimés pour l'étude de la matière organique, des paramètres texturaux et de la distribution granulométrique. Les dépôts laminés et instantanés au sein des échantillons ont été caractérisés. Ceci a permis d'estimer des chroniques de crues, ainsi que des variations biophysico-chimiques à l'échelle de la saison. L'imagerie hyperspectrale couplée à des méthodes d'analyse des données sont donc des outils performants pour l'étude des archives naturelles à des résolutions temporelles fines. L'approfondissement des approches proposées dans ces travaux permettra d'étudier de multiples archives pour caractériser des évolutions à l'échelle d'un ou de plusieurs bassin(s) versant(s)The evolution of the environment and climate are, currently, the focus of all attention. The impacts of the activities of present and past societies on the environment are in particular questioned in order to better anticipate the implications of our current activities on the future. Better describing past environments and their evolutions are possible thanks to the study of many natural recorders (sediments, speleothems, tree rings, corals). Thanks to them, it is possible to characterize biological-physical-chemical evolutions at di erent temporal resolutions and for di erent periods. The high resolution understood here as the su cient resolution for the study of the environment in connection with the evolution of societies constitutes the main lock of the study of these natural archives in particular because of the analytical capacity devices that can only rarely see ne inframillimetre structures. This work is built on the assumption that the use of hyperspectral sensors (VNIR, SWIR, LIF) coupled with relevant statistical methods should allow access to the spectral and therefore biological-physical-chemical contained in these natural archives at a spatial resolution of a few tens of micrometers and, therefore, to propose methods to reach the high temporal resolution (season). Besides, to obtain reliable estimates, several imaging sensors and linear spectroscopy (XRF, TRES) are used with their own characteristics (resolutions, spectral ranges, atomic/molecular interactions). These analytical methods are used for surface characterization of sediment cores. These micrometric spectral analyses are mapped to usual millimeter geochemical analyses. Optimizing the complementarity of all these data involves developing methods to overcome the di culty inherent in coupling data considered essentially dissimilar (resolutions, spatial shifts, spectral non-recovery). Thus, four methods were developed. The rst consists in combining hyperspectral and usual methods for the creation of quantitative predictive models. The second allows the spatial registration of di erent hyperspectral images at the lowest resolution. The third focuses on their merging with the highest of the resolutions. Finally, the last one focuses on deposits in sediments (laminae, oods, tephras) to add a temporal dimension to our studies. Through all this information and methods, multivariate predictive models were estimated for the study of organic matter, textural parameters and particle size distribution. The laminated and instantaneous deposits within the samples were characterized. These made it possible to estimate oods chronicles, as well as biological-physical-chemical variations at the season scale. Hyperspectral imaging coupled with data analysis methods are therefore powerful tools for the study of natural archives at ne temporal resolutions. The further development of the approaches proposed in this work will make it possible to study multiple archives to characterize evolutions at the scale of one or more watershed(s)
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Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.
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Au cours de cette thèse, nous nous sommes intéressés à deux principales problématiques de la télédétection spatiale de milieux urbains qui sont : le "démélange spectral " et la "fusion". Dans la première partie de la thèse, nous avons étudié le démélange spectral d'images hyperspectrales de scènes de milieux urbains. Les méthodes développées ont pour objectif d'extraire, d'une manière non-supervisée, les spectres des matériaux présents dans la scène imagée. Le plus souvent, les méthodes de démélange spectral (méthodes dites de séparation aveugle de sources) sont basées sur le modèle de mélange linéaire. Cependant, lorsque nous sommes en présence de paysage non-plat, comme c'est le cas en milieu urbain, le modèle de mélange linéaire n'est plus valide et doit être remplacé par un modèle de mélange non-linéaire. Ce modèle non-linéaire peut être réduit à un modèle de mélange linéaire-quadratique/bilinéaire. Les méthodes de démélange spectral proposées sont basées sur la factorisation matricielle avec contrainte de non-négativité, et elles sont conçues pour le cas particulier de scènes urbaines. Les méthodes proposées donnent généralement de meilleures performances que les méthodes testées de la littérature. La seconde partie de cette thèse à été consacrée à la mise en place de méthodes qui permettent la fusion des images multispectrale et hyperspectrale, afin d'améliorer la résolution spatiale de l'image hyperspectrale. Cette fusion consiste à combiner la résolution spatiale élevée des images multispectrales et la haute résolution spectrale des images hyperspectrales. Les méthodes mises en place sont des méthodes conçues pour le cas particulier de fusion de données de télédétection de milieux urbains. Ces méthodes sont basées sur des techniques de démélange spectral linéaire-quadratique et utilisent la factorisation en matrices non-négatives. Les résultats obtenus montrent que les méthodes développées donnent globalement des performances satisfaisantes pour la fusion des données hyperspectrale et multispectrale. Ils prouvent également que ces méthodes surpassent significativement les approches testées de la littératureIn this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
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Wahrman, Spencer A. "Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17473.
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Approved for public release; distribution is unlimitedGrand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both library spectra and regions of interest. Two sets of classification maps were produced using Mixture-Tuned Matched Filtering. The results were assessed visually and mathematically. Through visual inspection of the classification maps, increasing lodgepole pine mortality over time was observed. The results were quantified using confusion matrices comparing the classification results of the AVIRIS classified data and the IKONOS and QuickBird classified data. The comparison showed that change could be seen over time, but due to the short time period of the data the change was not as significant as expected.
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Hall, William D. "Exploration of Data Fusion between Polarimetric Radar and Multispectral Image Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17375.
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Approved for public release; distribution is unlimitedTypically, analysis of remote sensing data is limited to one sensor at a time which usually contains data from the same general portion of the electromagnetic spectrum. SAR and visible near infrared data of Monterey, CA, were analyzed and fused with the goal of achieving improved land classification results. A common SAR decomposition, the Pauli decomposition was performed and inspected. The SAR Pauli decomposition and the multispectral reflectance data were fused at the pixel level, then analyzed using multispectral classification techniques. The results were compared to the multispectral classifications using the SAR decomposition results for a basis of interpreting the changes. The combined dataset resulted in little to no quantitative improvement in land cover classification capability, however inspection of the classification maps indicated an improved classification ability with the combined data. The most noticeable increases in classification accuracy occurred in spatial regions where the land features were parallel to the SAR flight line. This dependence on orientation makes this fusion process more ideal for datasets with more consistent features throughout the scene.
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PISCINI, ALESSANDRO. "Neural-Network approach to multispectral and hyperspectral data analysis for volcanic monitoring." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/214160.
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Adams, Andrew J. "Multispectral persistent surveillance /." Online version of thesis, 2008. http://hdl.handle.net/1850/7070.
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Jahan, Farah. "Fusion of Hyperspectral and LiDAR Data for Land Cover Classification." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386555.
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Land cover classification has become increasingly important for making the plan to
overcome the problems of disorganized and uncontrolled development, the disappearance
of prime agricultural lands and deteriorating environmental quality by losing forest,
wildlife habitat, wetlands etc. Different remote sensing technologies capture different
properties e.g., spectral, shape, etc of ground objects. Nowadays, combined use of
multiple remote sensing technologies for land cover classification becomes popular.
Spectral image e.g., hyperspectral and lidar point cloud data are commonly used in
land cover classification. Among the spectral images, the hyperspectral image contains
detailed spectral responses of an object. On the contrary, light detection and ranging
(LiDAR) data capture structural information of an object. Thus hyperspectral and
LiDAR complement each other by accumulating information from land cover. Several
state-of-the-art methods were developed for fusing hyperspectral and LiDAR data for
land cover classification where the methods included feature extraction, feature fusion
and classification. Still, there are undiscovered properties of both modalities which
can contribute significantly in this domain. In this thesis, we discover a number
of effective ways for feature extraction from both hyperspectral and LiDAR data.
Furthermore, we propose two feature fusion techniques which are able to decrease
between-class correlation and increase within-class correlation while fusing features
from two modalities. Finally, a decision fusion approach e.g., ensemble classification
is incorporated for integrating prediction metrics. In this thesis, we propose three
different approaches for separating complex land cover classes by fusing hyperspectral
and LiDAR data. The effectiveness of these approaches is validated by experimenting
on two datasets e.g., Houston and GU datasets. The Houston dataset is a benchmark
dataset that contains fifteen landcover classes and distributed in 2013 IEEE GRSS Data
Fusion Contest. On the other hand, GU dataset consists of land cover classes and is
prepared from the hyperspectral and LiDAR data collected by the Spectral Imaging Lab
of Griffith University. We use two state-of-the-art classifiers e.g., random forest (RF)
and support vector machine (SVM) for classifying the features derived by our proposed
approaches.
In our first approach, we derive eight features from hyperspectral and LiDAR data.
Among them two are from hyperspectral and six are from LiDAR data. These eight
features show perfect complement property to hyperspectral features. In feature fusion,
we explore the effectiveness of layer stacking and principal component analysis (PCA)
where effective combination of features is investigated specially for PCA fusion.
In our second approach, we integrate three key tasks e.g., band-group fusion, multisource
fusion and generic feature (GF) extraction. In band-group fusion, we group
hyperspectral bands based on their joint entropy and structural similarity. We apply
PCA on each group and retain a few principal components and apply differential
attribute profiles (DAP) for extracting spatial features. The spatial and spectral features
from individual groups are fused using discriminant correlation analysis (DCA). In multisource
fusion, spatial features from hyperspectral and LiDAR are also fused by DCA.We
derive eight pixel-wise GF from hyperspectral and LiDAR data which are then arranged
sequentially to form an additional feature vector. Finally, we concatenate the features
generated by band-group fusion, multi-source fusion and generic feature extraction steps
to get a final signature.
In our third approach, we propose a novel feature extraction technique named inverse
coeffcient of variation (ICV) which explores the Gaussian probability of neighbourhood
between every pair of bands in hyperspectral data. We calculate ICV for each band
with respect to every other band and form an ICV cube. We derive spatial features (e.g.
DAP) from the first few principal components of both hyperspectral and ICV cube.
In addition, we derive GF from both hyperspectral and LiDAR data and then spatial
features from GF. Secondly, we propose a two-stream fusion approach where canonical
correlation analysis (CCA) is used as a basic fusion unit. In one stream pair-wise CCA
fusion of spectral features of hyperspectral with spatial features of both hyperspectral
and LiDAR takes place. In the other stream, pair-wise CCA fusion of ICV features with
spatial features derived from ICV, hyperspectral and LiDAR are performed. Thirdly, an
ensemble classification system is designed for decision fusion where features from twostream
fusion are distributed into random subsets, and then each subset is transformed
for improving feature quality, all are concatenated and classified. This process is
executed for several iteration. The final classification results are obtained by weighting
and aggregating the prediction metrics given by RF or applying majority voting on the
predicted classes given by SVM.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Sharma, Rajeev. "Using multispectral and hyperspectral satellite data for early detection of mountain pine beetle damage." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31064.
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Mountain pine beetle [MPB] [Dendroctonous ponderosae Hopk.) is the most serious pest of
mature lodgepole pine (Pinus contorta) in western North America. Three key research issues
important in developing satellite-based methods for early MPB damage detection and mapping are
examined in this thesis. Relevant questions relating to these issues are: i) is it possible to provide
information on MPB-attacked stands using satellite imagery at an earlier date than conventional
methods; ii) is spectral variability in mature lodgepole pine stands significant enough to warrant
consideration in MPB attack detection at a landscape level; and iii) are satellite-based hyperspectral
bands useful in forest tree species discrimination and early detection of MPB-attacked stands. The
first two questions were investigated using multispectral Landsat-7 ETM+ data; the third question was
investigated using EO-1 Hyperion hyperspectral data.
Using a multi-step deductive approach, MPB-attacked stands were identified with an
accuracy of 69% using the Landsat imagery, approximately four months earlier than would be
possible with conventional surveys. Significant spectral variability was found in mature stands of
lodgepole pine, Douglas-fir (Pseudotsuga menziesii) and spruce (Picea spp.) at the landscape level.
Among the three variables examined (stand age, site index and site ecology), site ecology (BEC
subzone/variants) had the largest influence on the spectral signatures of the three species. Douglas-fir,
lodgepole pine and spruce could be identified with an identification accuracy of 81.8%, 82.1% and
78.9%, respectively, using a subset of nine narrow bands from the Hyperion sensor, mainly
distributed in the 1500-1800 nm spectral region. Corresponding accuracies using Landsat data were
66.1%, 74.3% and 67.6%. Another set of nine spectral bands, optimized to identify MPB attack and
distributed mainly in the 900-1100 nm spectral region, resulted in identification accuracies of 81.7%
and 80.2% for MPB-attacked (mainly green-attack) and unattacked stands, respectively.
The results of this thesis demonstrate that early detection of MPB-attacked stands is possible
using multispectral and hyperspectral data at a scale and resolution to be of practical use to the forest
managers. Some of the results from this study have already been used operationally for planning the
harvest of MPB-killed trees.Forestry, Faculty of
Graduate
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Schneider, Sven. "A probablistic framework for classification and fusion of remotely sensed hyperspectral data." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9407.
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Reliable and accurate material identification is a crucial component underlying higher-level autonomous tasks within the context of autonomous mining. Such tasks can include exploration, reconnaissance and guidance of machines (e.g. autonomous diggers and haul trucks) to mine sites. This thesis focuses on the problem of classification of materials (rocks and minerals) using high spatial and high spectral resolution (hyperspectral) imagery, collected remotely from mine faces in operational open pit mines. A new method is developed for the classification of hyperspectral data including field spectra and imagery using a probabilistic framework and Gaussian Process regression. The developed method uses, for the first time, the Observation Angle Dependent (OAD) covariance function to classify high-dimensional sets of data. The performance of the proposed method of classification is assessed and compared to standard methods used for the classification of hyperspectral data. This is done using a staged experimental framework. First, the proposed method is tested using high-resolution field spectrometer data acquired in the laboratory and in the field. Second, the method is extended to work on hyperspectral imagery acquired in the laboratory and its performance evaluated. Finally, the method is evaluated for imagery acquired from a mine face under natural illumination and the use of independent spectral libraries to classify imagery is explored. A probabilistic framework was selected because it best enables the integration of internal and external information from a variety of sensors. To demonstrate advantages of the proposed GP-OAD method over existing, deterministic methods, a new framework is proposed to fuse hyperspectral images using the classified probabilistic outputs from several different images acquired of the same mine face. This method maximises the amount of information but reduces the amount of data by condensing all available information into a single map. Thus, the proposed fusion framework removes the need to manually select a single classification among many individual classifications of a mine face as the `best' one and increases the classification performance by combining more information. The methods proposed in this thesis are steps forward towards an automated mine face inspection system that can be used within the existing autonomous mining framework to improve productivity and efficiency. Last but not least the proposed methods will also contribute to increased mine safety.
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Mutlu, Muge. "Mapping surface fuels using LIDAR and multispectral data fusion for fire behavior modeling." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1118.
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Jordan, Johannes [Verfasser], Joachim [Akademischer Betreuer] Hornegger, and Joachim [Gutachter] Hornegger. "Interactive Analysis of Multispectral and Hyperspectral Image Data / Johannes Jordan ; Gutachter: Joachim Hornegger ; Betreuer: Joachim Hornegger." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2017. http://d-nb.info/1156780985/34.
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Chen, Hang. "Optical Encryption Techniques for Color Image and Hyperspectral Data." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0374.
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La sécurité est un problème important dans la transmission et le stockage de l'image, tandis que le cryptage est un moyen d'assurer la sécurité qui est requise dans de nombreuses applications (télévision par câble, la communication d'images militaires, systèmes d'imagerie confidentielle, etc.). Toutefois, à l'instar du message texte, les données images présentent des caractéristiques spéciales telles que la haute capacité, la redondance et la haute corrélation entre les pixels, et nécessite souvent une transmission et des traitements temps réel pour certaines applications. Construire un système rapide et efficace de cryptographie d'images suscite un intérêt considérable. C'est dans ce contexte qu’ont été menés ces travaux thèse qui portent sur l’élaboration d’un corrélateur optique en termes de cryptage/décryptage des données pour son implémentation dans un montage optique innovant. L’objectif de ces travaux est de réaliser un système optique de chiffrement sur la base d'exploitation de transformation optique et de générateurs chaotiques. L'idée originale des travaux consiste à exploiter la non-linéarité des systèmes chaotiques comme clés de chiffrement pour les systèmes optiques de chiffrement d'images multispectrales. Dans ces travaux de thèse, nous avons proposés et évalués plusieurs chiffrements d'images à base d’un système hyperchaotique et de transformées optiques (gyrator, Fourier, Baker , Arnold et Gerchberg- Saxton) à partir d’un processus de cryptage reposant sur une décomposition composants RVB et un encodage dans un flux dimensionnel d’images couleurs. L'originalité des solutions de chiffrement adoptée reposent sur l'exploitation de signaux réellement aléatoires à travers la mise en œuvre de générateurs hyperchaotiques pour la génération de données aléatoires sous forme images comme base de matrices de clés de chiffrement. En effet, ces générateurs présentent des propriétés et des caractéristiques fondamentales en termes de cryptage car il présente une non-linéarité, une imprédictibilité et une extrême sensibilité aux conditions initiales les rendant très intéressantes pour le développement de clés de chiffrement par flot. L’algorithme mis en œuvre permet d'extraire en temps réel les caractéristiques de texture dans les différentes bandes spectrales d'images en vue d’évaluer et de détecter les teneurs potentielles en information et dont les transmissions doivent être sécurisée via une transmission optiqueOptical information security is one of the most important research directions in information science and technology, especially in the field of copyright protection, confidential information transmission/storage and military remote sensing. Since double random phase encoding technology (DRPE) was proposed, optical image encryption technology has become the main topic of optical information security and it has been developed and studied deeply. Optical encryption techniques offer the possibility of high-speed parallel processing of two dimension image data and hiding information in many different dimensions. In this context, much significant research and investigation on optical image encryption have been presented based on DRPE or further optical operation, such as digital holography, Fresnel transform, gyrator transform. Simultaneously, the encrypted image has been extended from single gray image to double image, color image and multi-image. However, the hyperspectral image, as a significant element in military and commercial remote sensing, has not been deeply researched in optical encryption area until now. This work extends the optical encryption technology from color image to hyperspectral image. For better comprehension of hyperspectral image encryption, this work begins with the introduction and analysis of the characteristics of hyperspectral cube. Subsequently, several kinds of encryption schemes for color image, including symmetric and asymmetric cryptosystem, are presented individually. Furthermore, the optical encryption algorithms for hyperspectral cube are designed for securing both the spatial and spectral information simultaneously. Some numerical simulations are given to validate the performance of the proposed encryption schemes. The corresponding attack experiment results demonstrate the capability and robustness of the approaches designed in this work. The research in this dissertation provides reference for the further practicality of hyperspectral image encryption
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Polat, Songül. "Combined use of 3D and hyperspectral data for environmental applications." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSES049.
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La demande sans cesse croissante de solutions permettant de décrire notre environnement et les ressources qu'il contient nécessite des technologies qui permettent une description efficace et complète, conduisant à une meilleure compréhension du contenu. Les technologies optiques, la combinaison de ces technologies et un traitement efficace sont cruciaux dans ce contexte. Cette thèse se concentre sur les technologies 3D et les technologies hyper-spectrales (HSI). Tandis que les technologies 3D aident à comprendre les scènes de manière plus détaillée en utilisant des informations géométriques, topologiques et de profondeur, les développements rapides de l'imagerie hyper-spectrale ouvrent de nouvelles possibilités pour mieux comprendre les aspects physiques des matériaux et des scènes dans un large éventail d'applications grâce à leurs hautes résolutions spatiales et spectrales. Les travaux de recherches de cette thèse visent à l'utilisation combinée des données 3D et hyper-spectrales. Ils visent également à démontrer le potentiel et la valeur ajoutée d'une approche combinée dans le contexte de différentes applications. Une attention particulière est accordée à l'identification et à l'extraction de caractéristiques dans les deux domaines et à l'utilisation de ces caractéristiques pour détecter des objets d'intérêt.Plus spécifiquement, nous proposons différentes approches pour combiner les données 3D et hyper-spectrales en fonction des technologies 3D et d’imagerie hyper-spectrale (HSI) utilisées et montrons comment chaque capteur peut compenser les faiblesses de l'autre. De plus, une nouvelle méthode basée sur des critères de forme dédiés à la classification de signatures spectrales et des règles de décision liés à l'analyse des signatures spectrales a été développée et présentée. Les forces et les faiblesses de cette méthode par rapport aux approches existantes sont discutées. Les expérimentations réalisées, dans le domaine du patrimoine culturel et du tri de déchets plastiques et électroniques, démontrent que la performance et l’efficacité de la méthode proposée sont supérieures à celles des méthodes de machines à vecteurs de support (SVM).En outre, une nouvelle méthode d'analyse basée sur les caractéristiques 3D et hyper-spectrales est présentée. L'évaluation de cette méthode est basée sur un exemple pratique du domaine des déchet d'équipements électriques et électroniques (WEEE) et se concentre sur la séparation de matériaux comme les plastiques, les carte à circuit imprimé (PCB) et les composants électroniques sur PCB. Les résultats obtenus confirment qu'une amélioration des ré-sultats de classification a pu être obtenue par rapport aux méthodes proposées précédemment.L’avantage des méthodes et processus individuels développés dans cette thèse est qu’ils peuvent être transposé directement à tout autre domaine d'application que ceux investigué, et généralisé à d’autres cas d’étude sans adaptation préalableEver-increasing demands for solutions that describe our environment and the resources it contains, require technologies that support efficient and comprehensive description, leading to a better content-understanding. Optical technologies, the combination of these technologies and effective processing are crucial in this context. The focus of this thesis lies on 3D scanning and hyperspectral technologies. Rapid developments in hyperspectral imaging are opening up new possibilities for better understanding the physical aspects of materials and scenes in a wide range of applications due to their high spatial and spectral resolutions, while 3D technologies help to understand scenes in a more detailed way by using geometrical, topological and depth information. The investigations of this thesis aim at the combined use of 3D and hyperspectral data and demonstrates the potential and added value of a combined approach by means of different applications. Special focus is given to the identification and extraction of features in both domains and the use of these features to detect objects of interest. More specifically, we propose different approaches to combine 3D and hyperspectral data depending on the HSI/3D technologies used and show how each sensor could compensate the weaknesses of the other. Furthermore, a new shape and rule-based method for the analysis of spectral signatures was developed and presented. The strengths and weaknesses compared to existing approach-es are discussed and the outperformance compared to SVM methods are demonstrated on the basis of practical findings from the field of cultural heritage and waste management.Additionally, a newly developed analytical method based on 3D and hyperspectral characteristics is presented. The evaluation of this methodology is based on a practical exam-ple from the field of WEEE and focuses on the separation of materials like plastics, PCBs and electronic components on PCBs. The results obtained confirms that an improvement of classification results could be achieved compared to previously proposed methods.The claim of the individual methods and processes developed in this thesis is general validity and simple transferability to any field of application
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Aval, Josselin. "Automatic mapping of urban tree species based on multi-source remotely sensed data." Thesis, Toulouse, ISAE, 2018. http://www.theses.fr/2018ESAE0021/document.
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Avec l'expansion des zones urbaines, la pollution de l'air et l'effet d'îlot de chaleur augmentent, entraînant des problèmes de santé pour les habitants et des changements climatiques mondiaux. Dans ce contexte, les arbres urbains sont une ressource précieuse pour améliorer la qualité de l'air et promouvoir les îlot de fraîcheur. D'autre part, les canopées sont soumises à des conditions spécifiques dans l'environnement urbain, causant la propagation de maladies et la diminution de l'espérance de vie parmi les arbres. Cette thèse explore le potentiel de la télédétection pour la cartographie automatique des arbres urbains, de la détection des couronnes d'arbres à l'estimation des espèces, une tâche préliminaire essentielle pour la conception des futures villes vertes, et pour une surveillance efficace de la végétation. Fondé sur des données hyperspectrales aéroportées, panchromatiques et un modèle numérique de surface, le premier objectif de cette thèse consiste à tirer parti de plusieurs sources de données pour améliorer les cartes d'arbres urbains existants, en testant différentes stratégies de fusion (fusion de caractéristiques et fusion de décision). La nature des résultats nous a conduit à optimiser la complémentarité des sources. En particulier, le deuxième objectif est d'étudier en profondeur la richesse des données hyperspectrales, en développant une approche d'ensemble classifier fondée sur des indices de végétation, où les "classifier" sont spécifiques aux espèces. Enfin, la première partie a mis en évidence l'intérêt de distinguer les arbres de rue des autres structures d'arbres urbains. Dans un cadre de Marked Point Process, le troisième objectif est de détecter les arbres en alignement urbain. Par le premier objectif, cette thèse démontre que les données hyperspectrales sont le principal moteur de la précision de la prédiction des espèces. La stratégie de fusion au niveau de décision est la plus appropriée pour améliorer la performance en comparaison des données hyperspectrales seules, mais de légères améliorations sont obtenues (quelques %) en raison de la faible complémentarité des caractéristiques texturales et structurelles en plus des caractéristiques spectrales. L'approche d'ensemble classifier développée dans la deuxième partie permet de classer les espèces d'arbres à partir de références au sol, avec des améliorations significatives par rapport à une approche standard de classification au niveau des caractéristiques. Chaque classifieur d'espèces extrait reflète les attributs spectraux discriminants de l'espèce et peut être relié à l'expertise des botanistes. Enfin, les arbres de rue peuvent être cartographiés grâce au terme d'interaction des MPP proposé qui modélise leurs caractéristiques contextuelles (alignement et hauteurs similaires). De nombreuses améliorations doivent être explorées comme la délimitation plus précise de la couronne de l'arbre, et plusieurs perspectives sont envisageables après cette thèse, parmi lesquelles le suivi de l'état de santé des arbres urbainsWith the expansion of urban areas, air pollution and heat island effect are increasing, leading to state of health issues for the inhabitants and global climate changes. In this context, urban trees are a valuable resource for both improving air quality and promoting freshness islands. On the other hand, canopies are subject to specific conditions in the urban environment, causing the spread of diseases and life expectancy decreases among the trees. This thesis explores the potential of remote sensing for the automatic urban tree mapping, from the detection of the individual tree crowns to their species estimation, an essential preliminary task for designing the future green cities, and for an effective vegetation monitoring. Based on airborne hyperspectral, panchromatic and Digital Surface Model data, the first objective of this thesis consists in taking advantage of several data sources for improving the existing urban tree maps, by testing different fusion strategies (feature and decision level fusion). The nature of the results led us to optimize the complementarity of the sources. In particular, the second objective is to investigate deeply the richness of the hyperspectral data, by developing an ensemble classifiers approach based on vegetation indices, where the classifiers are species specific. Finally, the first part highlighted to interest of discriminating the street trees from the other structures of urban trees. In a Marked Point Process framework, the third objective is to detect trees in urban alignment. Through the first objective, this thesis demonstrates that the hyperspectral data are the main driver of the species prediction accuracy. The decision level fusion strategy is the most appropriate one for improving the performance in comparison the hyperspectral data alone, but slight improvements are obtained (a few percent) due to the low complementarity of textural and structural features in addition to the spectral ones. The ensemble classifiers approach developed in the second part allows the tree species to be classified from ground-based references, with significant improvements in comparison to a standard feature level classification approach. Each extracted species classifier reflects the discriminative spectral attributes of the species and can be related to the expertise of botanists. Finally, the street trees can be mapped thanks to the proposed MPP interaction term which models their contextual features (alignment and similar heights). Many improvements have to be explored such as the more accurate tree crown delineation, and several perspectives are conceivable after this thesis, among which the state of health monitoring of the urban trees
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Lee, Mark. "Benthic mapping of coastal waters using data fusion of hyperspectral imagery and airborne laser bathymetry." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000730.
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Jacob, Alexander. "Radar and Optical Data Fusion for Object Based Urban Land Cover Mapping." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-45978.
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The creation and classification of segments for object based urban land cover mapping is the key goal of this master thesis. An algorithm based on region growing and merging was developed, implemented and tested. The synergy effects of a fused data set of SAR and optical imagery were evaluated based on the classification results. The testing was mainly performed with data of the city of Beijing China. The dataset consists of SAR and optical data and the classified land cover/use maps were evaluated using standard methods for accuracy assessment like confusion matrices, kappa values and overall accuracy. The classification for the testing consists of 9 classes which are low density buildup, high density buildup, road, park, water, golf course, forest, agricultural crop and airport. The development was performed in JAVA and a suitable graphical interface for user friendly interaction was created parallel to the development of the algorithm. This was really useful during the period of extensive testing of the parameter which easily could be entered through the dialogs of the interface. The algorithm itself treats the pixels as a connected graph of pixels which can always merge with their direct neighbors, meaning sharing an edge with those. There are three criteria that can be used in the current state of the algorithm, a mean based spectral homogeneity measure, a variance based textural homogeneity measure and fragmentation test as a shape measure. The algorithm has 3 key parameters which are the minimum and maximum segments size as well as a homogeneity threshold measure which is based on a weighted combination of relative change due to merging two segments. The growing and merging is divided into two phases the first one is based on mutual best partner merging and the second one on the homogeneity threshold. In both phases it is possible to use all three criteria for merging in arbitrary weighting constellations. A third step is the check for the fulfillment of minimum size which can be performed prior to or after the other two steps. The segments can then in a supervised manner be labeled interactively using once again the graphical user interface for creating a training sample set. This training set can be used to derive a support vector machine which is based on a radial base function kernel. The optimal settings for the required parameters of this SVM training process can be found from a cross-validation grid search process which is implemented within the program as well. The SVM algorithm is based on the LibSVM java implementation. Once training is completed the SVM can be used to predict the whole dataset to get a classified land-cover map. It can be exported in form of a vector dataset. The results yield that the incorporation of texture features already in the segmentation is superior to spectral information alone especially when working with unfiltered SAR data. The incorporation of the suggested shape feature however doesn’t seem to be of advantage, especially when taking the much longer processing time into account, when incorporating this criterion. From the classification results it is also evident, that the fusion of SAR and optical data is beneficial for urban land cover mapping. Especially the distinction of urban areas and agricultural crops has been improved greatly but also the confusion between high and low density could be reduced due to the fusion.Dragon 2 Project
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Filiberti, Daniel Paul. "Combined Spatial-Spectral Processing of Multisource Data Using Thematic Content." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1066%5F1%5Fm.pdf&type=application/pdf.
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Lachaize, Marie. "Fusion de données : approche evidentielle pour le tri des déchets." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS113.
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Le tri automatique des déchets est un sujetcomplexe en raison de la diversité des objets et desmatériaux présents. Il nécessite un apport de donnéesvariées et hétérogènes. Cette thèse traite du problème defusion de données découlant d’un dispositif de troiscapteurs dont une caméra hyperspectrale dans ledomaine NIR. Nous avons étudié l’avantage d’utiliser lecadre des fonctions de croyance (BFT) tout au long de ladémarche de fusion en utilisant notamment la mesure deconflit comme un critère clé de notre approche. Dans unepremière partie, nous avons étudié l'intérêt de la BFTpour la classification multiclasse des donnéeshyperspectrales à partir d’Error Correcting OutputCodes (ECOC) qui consistent à séparer le problèmemulticlasse en un ensemble de sous-problèmes binairesplus simples à résoudre. Les questions de commentidéalement séparer le problème multiclasse (codage)ainsi que celle de la combinaison des réponses de cesproblèmes binaires (décodage) sont encore aujourd’huides questions ouvertes. Le cadre des fonctions decroyance permet de proposer une étape de décodage quimodélise chaque classifieur binaire comme une sourceindividuelle d'information grâce notamment à lamanipulation des hypothèses composées. Par ailleurs laBFT fournit des indices pour détecter les décisions peufiables ce qui permet une auto-évaluation de la méthoderéalisée sans vérité terrain. Dans une deuxième partietraitant de la fusion de données, nous proposons unedémarche ‘orientée-objet’ composée d’un module desegmentation et d’un module de classification afin defaire face aux problèmes d’échelle, de différences derésolutions et de recalage des capteurs. L’objectif estalors d’estimer une segmentation où les segmentscoïncident avec les objets individuels et sont labellisés entermes de matériau. Nous proposons une interactionentre les modules à base de validation mutuelle. Ainsi,d’une part la fiabilité de la labellisation est évaluée auniveau des segments, d’autre part l’information declassification interagit sur les segments initiaux pour serapprocher d’une segmentation au niveau « objet » : leconsensus (ou l’absence de consensus) parmi lesinformations de classification au sein d’un segment ouentre segments connexes permet de faire évoluer lesupport spatial vers le niveau objetAutomatic waste sorting is a complex matterbecause of the diversity of the objects and of the presentmaterials. It requires input from various andheterogeneous data. This PhD work deals with the datafusion problem derived from an acquisition devicecomposed of three sensors, including an hyperspectralsensor in the NIR field. We first studied the benefit ofusing the belief function theory framework (BFT)throughout the fusion approach, using in particularconflict measures to drive the process. We first studiedthe BFT in the multiclass classification problem createdby hyperspectral data. We used the Error CorrectingOutput Codes (ECOC) framework which consists inseparating the multiclass problem into several binaryones, simpler to solve. The questions of the idealdecomposition of the multiclass problem (coding) and ofthe answer combination coming from the binaryclassifiers (decoding) are still open-ended questions. Thebelief function framework allows us to propose adecoding step modelling each binary classifier as anindividual source of information, thanks to the possibilityof handling compound hypotheses. Besides, the BFTprovides indices to detect non reliable decisions whichallow for an auto-evaluation of the method performedwithout using any ground truth. In a second part dealingwith the data fusion,we propose an evidential version ofan object-based approach composed with a segmentationmodule and a classification module in order to tackle theproblems of the differences in scale, resolutions orregistrations of the sensors. The objective is then toestimate a relevant spatial support corresponding to theobjects while labelling them in terms of material. Weproposed an interactive approach with cooperationbetween the two modules in a cross-validation kind ofway. This way, the reliability of the labelling isevaluated at the segment level, while the classificationinformation acts on the initial segments in order toevolve towards an object level segmentation: consensusamong the classification information within a segment orbetween adjacent regions allow the spatial support toprogressively reach object level
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Tusa, jumbo Eduardo Alejandro. "Apport de la fusion LiDAR - hyperspectral pour la caractérisation géométrique et radiométrique des arbres." Thesis, Université Grenoble Alpes, 2020. https://tel.archives-ouvertes.fr/tel-03212453.
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Les forêts de montagne fournissent des services écosystémiques environnementaux (SEE) aux communautés: fourniture de paysages récréatifs, protection contre les risques naturels, soutien à la conservation de la biodiversité, entre autres. La préservation de ces SEE dans l'espace et dans le temps nécessite une bonne caractérisation des ressources. Surtout en montagne, les peuplements sont très hétérogènes et la récolte du bois est économiquement possible grâce à des arbres de plus grande valeur. C'est pourquoi nous voulons pouvoir cartographier chaque arbre et estimer ses caractéristiques, dont la qualité, qui est liée à sa forme et ses conditions de croissance. Les inventaires de terrain ne sont pas en mesure de fournir une couverture mur à mur d'informations détaillées au niveau des arbres à grande échelle. D'un autre côté, les outils de télédétection semblent être une technologie prometteuse en raison de la rapidité et des coûts abordables pour l'étude des zones forestières. Les données LiDAR fournissent des informations détaillées sur la distribution verticale et l'emplacement des arbres, mais elles sont limitées pour la cartographie des espèces. Les données hyperspectrales sont associées aux caractéristiques d'absorption dans le spectre de réflectance du couvert, mais ne sont pas efficaces pour caractériser la géométrie des arbres. Les systèmes hyperspectraux et LiDAR fournissent des données indépendantes et complémentaires qui sont pertinentes pour l'évaluation des attributs biophysiques et biochimiques des zones forestières. Cette thèse de doctorat porte sur la fusion de LiDAR et de données hyperspectrales pour caractériser les arbres forestiers individuels. L'idée maîtresse est d'améliorer les méthodes pour obtenir des informations forestières au niveau de l'arbre en extrayant des caractéristiques géométriques et radiométriques. Les contributions de ce travail de recherche reposent sur: i) un examen mis à jour des méthodes de fusion de données de LiDAR et des données hyperspectrales pour la surveillance des forêts, ii) un algorithme de segmentation 3D amélioré pour délimiter les couronnes d'arbres individuelles basé sur Adaptive Mean Shift (AMS3D) et un ellipsoïde modèle de forme de couronne, iii) un critère de sélection des caractéristiques basé sur le score aléatoire des forêts, cross-validation à 5 folds et une fonction d'erreur cumulative pour la classification des espèces d'arbres forestiers. Les deux principales méthodes utilisées pour obtenir des informations forestières au niveau des arbres sont testées avec des données de télédétection acquises dans les Alpes françaisesMountain forests provide environmental ecosystem services (EES) to communities: supplying of recreational landscapes, protection against natural hazards, supporting biodiversity conservation, among others. The preservation of these EES through space and time requires a good characterization of the resources. Especially in mountains, stands are very heterogeneous and timber harvesting is economically possible thanks to trees of higher value. This is why we want to be able to map each tree and estimate its characteristics, including quality, which is related to its shape and growth conditions. Field inventories are not able to provide a wall to wall cover of detailed tree-level information on a large scale. On the other hand, remote sensing tools seem to be a promising technology because of the time efficient and the affordable costs for studying forest areas. LiDAR data provide detailed information from the vertical distribution and location of the trees, but it is limited for mapping species. Hyperspectral data are associated to absorption features in the canopy reflectance spectrum, but is not effective for characterizing tree geometry. Hyperspectral and LiDAR systems provide independent and complementary data that are relevant for the assessment of biophysical and biochemical attributes of forest areas. This PhD thesis deals with the fusion of LiDAR and hyperspectral data to characterize individual forest trees. The leading idea is to improve methods to derive forest information at tree-level by extracting geometric and radiometric features. The contributions of this research work relies on: i) an updated review of data fusion methods of LiDAR and hyperspectral data for forest monitoring, ii) an improved 3D segmentation algorithm for delineating individual tree crowns based on Adaptive Mean Shift (AMS3D) and an ellipsoid crown shape model, iii) a criterion for feature selection based on random forests score, $5$-fold cross validation and a cumulative error function for forest tree species classification. The two main methods used to derive forest information at tree level are tested with remote sensing data acquired in the French Alps
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Hunger, Sebastian, Pierre Karrasch, and Christine Wessollek. "Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure." SPIE, 2016. https://tud.qucosa.de/id/qucosa%3A34859.
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The European Water Framework Directive (Directive 2000/60/EC) is a mandatory agreement that guides the member states of the European Union in the field of water policy to fulfil the requirements for reaching the aim of the good ecological status of water bodies. In the last years several work ows and methods were developed to determine and evaluate the haracteristics and the status of the water bodies. Due to their area measurements remote sensing methods are a promising approach to constitute a substantial additional value. With increasing availability of optical and radar remote sensing data the development of new methods to extract information from both types of remote sensing data is still in progress. Since most limitations of these data sets do not agree the fusion of both data sets to gain data with higher spectral resolution features the potential to obtain additional information in contrast to the separate processing of the data. Based thereupon this study shall research the potential of multispectral and radar remote sensing data and the potential of their fusion for the assessment of the parameters of water body structure. Due to the medium spatial resolution of the freely available multispectral Sentinel-2 data sets especially the surroundings of the water bodies and their land use are part of this study. SAR data is provided by the Sentinel-1 satellite. Different image fusion methods are tested and the combined products of both data sets are evaluated afterwards. The evaluation of the single data sets and the fused data sets is performed by means of a maximum-likelihood classification and several statistical measurements. The results indicate that the combined use of different remote sensing data sets can have an added value.
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Waheed, Tahir. "Artificial intelligence analysis of hyperspectral remote sensing data for management of water, weed, and nitrogen stresses in corn fields." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86060.
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This study investigated the possibility of using ground-based remotely sensed hyperspectral observations with a special emphasis on detection of water, weed and nitrogen stresses contributing towards in-season decision support for precision crop management (PCM).A three factor split-split-plot experiment, with four randomized blocks as replicates, was established during the growing seasons of 2003 and 2004. Corn (Zea mays L.) hybrid DKC42-22 was grown because this hybrid is a good performer on light soils in Quebec. There were twelve 12 x 12m plots in a block (one replication per treatment per block) and the total number of plots was 48. Water stress was the main factor in the experiment. A drip irrigation system was laid out and each block was split into irrigated and non-irrigated halves. The second main factor of the experiment was weeds with two levels i.e. full weed control and no weed control. Weed treatments were assigned randomly by further splitting the irrigated and non-irrigated sub-blocks into two halves. Each of the weed treatments was furthermore split into three equal sub-sub-plots for nitrogen treatments (third factor of the experiment). Nitrogen was applied at three levels i.e. 50, 150 and 250 kg N ha-1 (Quebec norm is between 120-160 kg N ha-1).
The hyperspectral data were recorded (spectral resolution = 1 nm) mid-day (between 1000 and 1400 hours) with a FieldSpec FR spectroradiometer over a spectral range of 400-2500 run at three growth stages namely: early growth, tasseling and full maturity, in each of the growing season.
There are two major original contributions in this thesis: First is the development of a hyperspectral data analysis procedure for separating visible (400-700 nm), near-infrared (700-1300 nm) and mid-infrared (1300-2500 nm) regions of the spectrum for use in discriminant analysis procedure. In addition, of all the spectral band-widths analyzed, seven waveband-aggregates were identified using STEPDISC procedure, which were the most effective for classifying combined water, weed, and nitrogen stress. The second contribution is the successful classification of hyperspectral observations acquired over an agricultural field, using three innovative artificial intelligence approaches; support vector machines (SVM), genetic algorithms (GA) and decision tree (DT) algorithms. These AI approaches were used to evaluate a combined effect of water, weed and nitrogen stresses in corn and of all the three AI approaches used, SVM produced the best results (overall accuracy ranging from 88% to 100%).
The general conclusion is that the conventional statistical and artificial intelligence techniques used in this study are all useful for quickly mapping combined affects of irrigation, weed and nitrogen stresses (with overall accuracies ranging from 76% to 100%). These approaches have strong potential and are of great benefit to those investigating the in-season impact of irrigation, weed and nitrogen management for corn crop production and other environment related challenges.
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Rocha, de Oliveira Rodrigo. "Development and implementation of strategies for process data fusion, modelling and control." Doctoral thesis, Universitat de Barcelona, 2022. http://hdl.handle.net/10803/673296.
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With the emergence of Industry 4.0 and the increasing availability of sensors and data acquisition systems, modern manufacturing processes are now generating large amounts of process data on a scale as never seen before. During the past few decades, the intense development of powerful data-driven methodologies for process analytics has demonstrated the importance of multivariate data analysis for this field. Still, new strategies inspired by current methodologies and yet to be developed will continuously be required to tackle new challenges posed by the digital revolution in process analytics.
This thesis has been focused on the development and application of chemometric tools for process analytical technology (PAT) and includes approaches for process monitoring, modeling and control of batch processes. All the methodology proposed has been tested on real batch processes of diverse nature monitored with sensor of different typology. The chemometric tools developed in this thesis are meant to be used in two different contexts: a) process monitoring, modeling, and control using spectroscopic probes and process sensors, and b) process monitoring using hyperspectral images.
In the context of process monitoring using spectroscopic probes and process sensors, different methodologies have been designed to handle information coming from synchronized and non-synchronized batch process data. For synchronized batch process data, new strategies for offline and online Multivariate Statistical Process Control (MSPC) have been designed. Offline MSPC models, meant to control complete batches, were built based on information coming from original sensor variables or from compressed spectral information, issued from multivariate exploratory and resolution analysis outputs. Online process control methodologies were based on the use of local MSPC models built exploring the effect of different designs of process time windows onto the capacity to discriminate between observations following normal operation conditions (NOC) and showing an abnormal behavior. For non-synchronized batch data, a novel batch synchronization-free online MSPC methodology for tracking process evolution and control was proposed based on the idea of a global batch process trajectory and the use of local MSPC models.
A clear improvement of the results linked to all MSPC scenarios is linked to the use of new mid-level data fusion strategies. The novel contribution in this thesis is the extension of the idea of data fusion to incorporate both diverse sensor outputs and diverse model outputs issued from the same sensor, but related to different modeling tasks. These model outputs, which are much more specific than mere compressed scores, help significantly to tune the information introduced in the MSPC models and to a better interpretation of the sources of abnormal process behavior.
The chemometric solutions proposed for process monitoring using hyperspectral images (HSI) were mainly oriented to take advantage of the spatial information of the
measurement for the qualitative and quantitative heterogeneity assessment in blending processes. The qualitative description of heterogeneity is linked to HSI unmixing analysis, which provides pure component distribution maps that offer a good visual representation of the evenness in the spatial distribution of the different materials in the blending formulation. The quantitative characterization of heterogeneity is obtained from the variographic analysis of the distribution maps and results in two indices: the Global Heterogeneity Index (GHI), related to the scatter of the individual pixel concentration values, and the Distributional Uniformity Index (DUI), describing the distributional heterogeneity, usually overlooked in traditional approaches, that expresses the evenness in the spatial distribution of the different materials forming a blend. These indices have been proven to be a powerful process analytical tool to characterize the heterogeneity in blending processes monitored atline and inline with NIR-HSI. For image-based inline process monitoring, an extension of this methodology, called SWiVIA (Sliding Window Variographic Image Analysis), has been adapted for the continuous assessment of heterogeneity in real-time blending process monitoring. The versatility of the SWiVIA methodology enables heterogeneity assessment at the time resolution and spatial scale of scrutiny required for the blending application of interest.Amb l'arribada de la Indústria 4.0 i la creixent disponibilitat de sensors i sistemes d'adquisició de dades, els processos de fabricació moderns generen quantitats ingents de dades de procés a una escala mai vista. Durant les últimes dècades, el desenvolupament continuat de metodologies d'anàlisi de processos basades en la interpretació directa de la mesura ha confirmat la importància de l'anàlisi multivariant de dades en aquest camp. Tot i així, caldrà desenvolupar noves aproximacions inspirades en metodologies existents o encara per descobrir per afrontar els nous reptes que planteja la revolució digital en l'anàlisi de processos. Aquesta tesi s'ha centrat en el desenvolupament i aplicació d'eines quimiomètriques lligades a la tecnologia analítica de processos (PAT) per al seguiment, modelització i control de processos per lots. Tota la metodologia proposada ha estat provada en processos reals de diversa naturalesa monitorats amb sensors de diferents tipologies. Les eines quimiomètriques desenvolupades en aquesta tesi estan pensades per ser utilitzades en dos contextos diferents: a) el seguiment, modelització i control de processos mitjançant sondes espectroscòpiques i sensors de procés, i b) el seguiment de processos mitjançant imatges hiperespectrals. En el context del monitoratge de processos mitjançant sondes espectroscòpiques i sensors de procés, s'han dissenyat diferents metodologies per gestionar la informació procedent de dades de procés per lots sincronitzats i no sincronitzats. Per a dades de lots sincronitzats, s'han dissenyat noves estratègies per al control estadístic multivariant de processos (MSPC, Multivariate Statistical Process Control) offline i online. Els models MSPC offline, destinats a controlar lots complets, es van construir a partir d'informació associada a variables originals de sensors o d'informació espectral comprimida, procedent de resultats de models d'anàlisi exploratòria i de resolució multivariant. Les metodologies de control de processos online es van basar en l'ús de models locals de MSPC construïts explorant l'efecte de diferents dissenys de finestres de temps de procés sobre la capacitat de discriminar observacions seguint condicions normals d’operació (NOC, Normal Operation Conditions) d’observacions amb un comportament anòmal. Per a les dades de lots no sincronitzats, es va proposar una nova metodologia MSPC online exempta de l’etapa de sincronització per fer un seguiment de l'evolució i el control del procés basada en l’ús d'una trajectòria global del procés per lots, que serveix per a la construcció de models locals de MSPC. Una millora clara dels resultats associada a tots els escenaris de models MSPC està vinculada a l'ús de noves estratègies de fusió de dades de nivell intermedi (mid-level data fusion). La nova contribució d'aquesta tesi és l'extensió de la idea de fusió de dades a la incorporació tant de respostes de sensors diversos com de resultats de models multivariants obtinguts de respostes d’un mateix sensor, però relacionats amb diferents tasques de modelització. Aquests resultats de models multivariants, que aporten informació molt més específica que els scores de PCA, per exemple, permeten una tria més acurada de la informació que s’introdueix en els models MSPC i faciliten una millor interpretació de les causes de comportaments anòmals en el procés. Les solucions quimiomètriques proposades per al seguiment de processos mitjançant imatges hiperespectrals (HSI, Hyperspectral Images) es van orientar principalment a aprofitar la informació espacial de la mesura per a l'avaluació qualitativa i quantitativa de l'heterogeneïtat en els processos de mescla. La descripció qualitativa de l'heterogeneïtat està vinculada al resultat de l’anàlisi de resolució multivariant de les dades HSI, que proporciona mapes de distribució de components purs que ofereixen una bona representació visual de la uniformitat en la distribució espacial dels diferents materials en la mescla estudiada. La caracterització quantitativa de l'heterogeneïtat s'obté de l'anàlisi variogràfica dels mapes de distribució i està basada en dos índexs: l'índex d'heterogeneïtat global (GHI, Global Heterogeneity Index), relacionat amb la dispersió dels valors de concentració dels píxels individuals, i l'índex d'uniformitat distribucional (DUI, Distributional Uniformity Index), que descriu l'heterogeneïtat distribucional, normalment ignorada en plantejaments tradicionals, que expressa el grau d’uniformitat en la distribució espacial dels diferents materials que formen una mescla. S'ha demostrat que aquests índexs són una eina PAT potent per caracteritzar l'heterogeneïtat dels processos de mescla seguits amb mesures discretes o en temps real mitjançant imatgeria hiperespectral d’infraroig proper (NIR-HSI). Per al seguiment de processos en temps real basat en imatges, s'ha adaptat una extensió d'aquesta metodologia, anomenada SWiVIA (Sliding Window Variographic Image Analysis – Anàlisi variogràfica d’imatges basada en finestres mòbils), per a l'avaluació en temps real de l'heterogeneïtat en el seguiment continu de processos. La versatilitat de la metodologia SWiVIA permet l'avaluació de l'heterogeneïtat amb la resolució temporal i l'escala espacial d'escrutini desitjada segons les característiques del procés de mescla estudiat.
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Giordano, Sébastien. "Démélange d'images radar polarimétrique par séparation thématique de sources." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1176/document.
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Cette thèse s'inscrit dans le contexte de l'amélioration de la caractérisation de l'occupation du sol à partir d'observations de télédétection de natures très différentes : le radar polarimétrique et les images optiques multispectrales. Le radar polarimétrique permet la détermination de mécanismes de rétrodiffusion provenant de théorèmes de décomposition de l'information polarimétrique utiles à la classification des types d'occupation du sol. Cependant ces décompositions sont peu compréhensibles lorsque que plu- sieurs classes thématiques co-existent dans des proportions très variables au sein des cellules de résolution radar. Le problème est d'autant plus important que le speckle inhérent à l'imagerie radar nécessite l'estimation de ces paramètres sur des voisinages locaux. Nous nous interrogeons alors sur la capacité des données optiques multispectrales sensiblement plus résolues spatialement que le radar polarimétrique à améliorer la compréhension des mécanismes radar. Pour répondre à cette question, nous mettons en place une méthode de démélange des images radar polarimétrique par séparation thématique de sources. L'image optique peut être considérée comme un paramètre de réglage du radar fournissant une vue du mélange. L'idée générale est donc de commencer par un démélange thématique (décomposer l'information radar sur les types d'occupation du sol) avant de réaliser les décompositions polarimétriques (identifier des mécanismes de rétrodiffusion).Dans ce travail nous proposons d'utiliser un modèle linéaire et présentons un algorithme pour réaliser le démélange thématique. Nous déterminons ensuite la capacité de l'algorithme de démé- lange à reconstruire le signal radar observé. Enfin nous évaluons si l'information radar démélangée contient de l'information thématique pertinente. Cette évaluation est réalisée sur des données simulées que nous avons générées et sur des données Radarsat-2 complètement polarimétriques pour un cas d'application de mélange sol nu/forêt. Les résultats montrent que, malgré le speckle, la reconstruction est valable. Il est toujours possible d'estimer localement des bases thématiques permettant de décomposer l'information radar polarimétrique puis de reconstruire le signal observé. Cet algorithme de démélange permet aussi d'assimiler de l'information portée par les images optiques. L'évaluation de la pertinence thématique des bases de la décomposition est plus problématique. Les expériences sur des données simulées montrent que celles-ci représentent bien l'information thématique souhaitée, mais que cette bonne estimation est dépendante de la nature des types thématiques et de leurs proportions de mélange. Cette méthode nécessite donc des études complémentaires sur l'utilisation de méthodes d'estimation plus robustes aux statistiques des images radar. Son application à des images radar de longueur d'onde plus longue pourrait permettre, par exemple, une meilleure estimation du volume de végétation dans le contexte de forêts ouvertesLand cover is a layer of information of significant interest for land management issues. In this context, combining remote sensing observations of different types is expected to produce more reliable results on land cover classification. The objective of this work is to explore the use of polarimetric radar images in association with co-registered higher resolution optical images. Extracting information from a polarimetric representation consists in decomposing it with target decomposition algorithms. Understanding these mechanisms is challenging as they are mixed inside the radar cell resolution but it is the key to producing a reliable land cover classification. The problem while using these target decomposition algorithms is that average physical parameters are obtained. As a result, each land cover type of a mixed pixel might not be well described by the average polarimetric parameters. The effect is all the more important as speckle affecting radar observations requires a local estimation of the polarimetric matrices. In this context, we chose to assess whether optical images can improve the understanding of radar images at the observation scale so as to retrieve more information. Spatial and spectral unmixing methods, traditionally designed for optical image fusion, were found to be an interesting framework. As a consequence, the idea of unmixing physical radar scattering mechanisms with the optical images is proposed. The original method developed is the decomposition of the polarimetric information, based on land cover type. This thematic decomposition is performed before applying usual target decomposition algorithms. A linear mixing model for radar images and an unmixing algorithm are proposed in this document. Having pointed out that the linear unmixing model is able to split off polarimetric information on a land cover type basis, the information contained in the unmixed matrices is evaluated. The assesment is carried out with generated simulated data and polarimetric radar images from the Radarsat-2 satellite. For this experiment, textit {Bare soil} and textit {Forested area} were considered for land cover types. It was found that despite speckle the reconstructed radar information after the unmixing is statically relevant with the observations. Moreover, the unmixing algorithm is capable of assimilating information from optical images. The question whether the unmixed radar images contain relevant thematic information is more challenging. Results on real and simulated data show that this capacity depends on the types of land cover considered and their respective proportions. Future work will be carried out to make the estimation step more robust to speckle and to test this unmixing algorithm on longer wavelength radar images. In this case, this method could be used to have a better estimation of vegetation biomass in the context of open forested areas
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Elatawneh, Alata [Verfasser], Thomas F. [Akademischer Betreuer] Knoke, and Xiaoxiang [Akademischer Betreuer] Zhu. "Investigations into the potentials of hyperspectral and multi-seasonal / multispectral satellites data for forest parameter determination / Alata Elatawneh. Gutachter: Xiaoxiang Zhu ; Thomas F. Knoke. Betreuer: Thomas F. Knoke." München : Universitätsbibliothek der TU München, 2015. http://d-nb.info/1074999517/34.
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Benmoussat, Mohammed Seghir. "Hyperspectral imagery algorithms for the processing of multimodal data : application for metal surface inspection in an industrial context by means of multispectral imagery, infrared thermography and stripe projection techniques." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4347/document.
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Le travail présenté dans cette thèse porte sur l'inspection de surfaces métalliques industrielles. Nous proposons de généraliser des méthodes de l'imagerie hyperspectrale à des données multimodales comme des images optiques multi-canales, et des images thermographiques multi-temporelles. Dans la première application, les cubes de données sont construits à partir d'images multi-composantes pour détecter des défauts de surface. Les meilleures performances sont obtenues avec les éclairages multi-longueurs d'ondes dans le visible et le proche IR, et la détection du défaut en utilisant l'angle spectral, avec le spectre moyen comme référence. La deuxième application concerne l'utilisation de l'imagerie thermique pour l'inspection de pièces métalliques nucléaires afin de détecter des défauts de surface et sub-surface. Une approche 1D est proposée, basée sur l'utilisation du kurtosis pour sélectionner la composante principale parmi les premières obtenues après réduction des données avec l’ACP. La méthode proposée donne de bonnes performances avec des données non-bruitées et homogènes, cependant la SVD avec les algorithmes de détection d'anomalies est très robuste aux perturbations. Finalement, une approche, basée sur les techniques d'analyse de franges et la lumière structurée est présentée, dans le but d'inspecter des surfaces métalliques à forme libre. Après avoir déterminé les paramètres décrivant les modèles de franges sinusoïdaux, l'approche proposée consiste à projeter une liste de motifs déphasés et à calculer l'image de phase des motifs enregistrés. La localisation des défauts est basée sur la détection et l'analyse des franges dans les images de phaseThe work presented in this thesis deals with the quality control and inspection of industrial metallic surfaces. The purpose is the generalization and application of hyperspectral imagery methods for multimodal data such as multi-channel optical images and multi-temporal thermographic images. In the first application, data cubes are built from multi-component images to detect surface defects within flat metallic parts. The best performances are obtained with multi-wavelength illuminations in the visible and near infrared ranges, and detection using spectral angle mapper with mean spectrum as a reference. The second application turns on the use of thermography imaging for the inspection of nuclear metal components to detect surface and subsurface defects. A 1D approach is proposed based on using the kurtosis to select 1 principal component (PC) from the first PCs obtained after reducing the original data cube with the principal component analysis (PCA) algorithm. The proposed PCA-1PC method gives good performances with non-noisy and homogeneous data, and SVD with anomaly detection algorithms gives the most consistent results and is quite robust to perturbations such as inhomogeneous background. Finally, an approach based on fringe analysis and structured light techniques in case of deflectometric recordings is presented for the inspection of free-form metal surfaces. After determining the parameters describing the sinusoidal stripe patterns, the proposed approach consists in projecting a list of phase-shifted patterns and calculating the corresponding phase-images. Defect location is based on detecting and analyzing the stripes within the phase-images
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Berger, Christian [Verfasser], Sören Mathias [Gutachter] Hese, Christiane [Gutachter] Schmullius, and Hannes [Gutachter] Taubenböck. "Fusion of high spatial resolution multispectral & object height data for urban environmental monitoring : methods & applications / Christian Berger ; Gutachter: Sören Mathias Hese, Christiane Schmullius, Hannes Taubenböck." Jena : Friedrich-Schiller-Universität Jena, 2017. http://d-nb.info/1177597705/34.
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Grohnfeldt, Claas Hendrik [Verfasser], Xiaoxiang [Akademischer Betreuer] [Gutachter] Zhu, Richard [Gutachter] Bamler, and Naoto [Gutachter] Yokoya. "Multi-sensor Data Fusion for Multi- and Hyperspectral Resolution Enhancement Based on Sparse Representations / Claas Hendrik Grohnfeldt ; Gutachter: Richard Bamler, Xiaoxiang Zhu, Naoto Yokoya ; Betreuer: Xiaoxiang Zhu." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1140835181/34.
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Bacher, Raphael. "Méthodes pour l'analyse des champs profonds extragalactiques MUSE : démélange et fusion de données hyperspectrales ;détection de sources étendues par inférence à grande échelle." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAT067/document.
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Ces travaux se placent dans le contexte de l'étude des champs profonds hyperspectraux produits par l'instrument d'observation céleste MUSE. Ces données permettent de sonder l'Univers lointain et d'étudier les propriétés physiques et chimiques des premières structures galactiques et extra-galactiques. La première problématique abordée dans cette thèse est l'attribution d'une signature spectrale pour chaque source galactique. MUSE étant un instrument au sol, la turbulence atmosphérique dégrade fortement le pouvoir de résolution spatiale de l'instrument, ce qui génère des situations de mélange spectral pour un grand nombre de sources. Pour lever cette limitation, des approches de fusion de données, s'appuyant sur les données complémentaires du télescope spatial Hubble et d'un modèle de mélange linéaire, sont proposées, permettant la séparation spectrale des sources du champ. Le second objectif de cette thèse est la détection du Circum-Galactic Medium (CGM). Le CGM, milieu gazeux s'étendant autour de certaines galaxies, se caractérise par une signature spatialement diffuse et de faible intensité spectrale. Une méthode de détection de cette signature par test d'hypothèses est développée, basée sur une stratégie de max-test sur un dictionnaire et un apprentissage des statistiques de test sur les données. Cette méthode est ensuite étendue pour prendre en compte la structure spatiale des sources et ainsi améliorer la puissance de détection tout en conservant un contrôle global des erreurs. Les codes développés sont intégrés dans la bibliothèque logicielle du consortium MUSE afin d'être utilisables par l'ensemble de la communauté. De plus, si ces travaux sont particulièrement adaptés aux données MUSE, ils peuvent être étendus à d'autres applications dans les domaines de la séparation de sources et de la détection de sources faibles et étenduesThis work takes place in the context of the study of hyperspectral deep fields produced by the European 3D spectrograph MUSE. These fields allow to explore the young remote Universe and to study the physical and chemical properties of the first galactical and extra-galactical structures.The first part of the thesis deals with the estimation of a spectral signature for each galaxy. As MUSE is a terrestrial instrument, the atmospheric turbulences strongly degrades the spatial resolution power of the instrument thus generating spectral mixing of multiple sources. To remove this issue, data fusion approaches, based on a linear mixing model and complementary data from the Hubble Space Telescope are proposed, allowing the spectral separation of the sources.The second goal of this thesis is to detect the Circum-Galactic Medium (CGM). This CGM, which is formed of clouds of gas surrounding some galaxies, is characterized by a spatially extended faint spectral signature. To detect this kind of signal, an hypothesis testing approach is proposed, based on a max-test strategy on a dictionary. The test statistics is learned on the data. This method is then extended to better take into account the spatial structure of the targets, thus improving the detection power, while still ensuring global error control.All these developments are integrated in the software library of the MUSE consortium in order to be used by the astrophysical community.Moreover, these works can easily be extended beyond MUSE data to other application fields that need faint extended source detection and source separation methods
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Burian, František. "Tvorba multispektrálních map v mobilní robotice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233689.
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The dissertation deals with utilisation of multispectral optical measurement for data fusion that may be used for visual telepresence and indoor/outdoor mapping by heterogeneous mobile robotic system. Optical proximity sensors, thermal imagers, and tricolour cameras are used for the fusion. The described algorithms are optimised to work in real-time and implemented on CASSANDRA robotic system made by our robotic research group.
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Martins, George Deroco [UNESP]. "Inferência dos níveis de infecção por Nematoides na cultura cafeeira a partir de dados de sensoriamento remoto adquiridos em multiescala." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148760.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Os nematoides são importantes fitoparasitas que se constituem em um problema sério para o cultivo do café no Brasil. Como a ocorrência de nematoides no sistema radicular do cafeeiro causa desequilíbrios nutricionais na planta que provocam variações na resposta espectral da folha e define uma configuração espacial característica às áreas infectadas, o objetivo desta pesquisa avaliar o potencial de dados de sensoriamento remoto adquiridos em multiescala para discriminar e mapear o café sadio, em estágio inicial de infecção e severamente infectado. A pesquisa foi desenvolvida em três áreas experimentais, localizadas no sul do estado de Minas Gerais, nas quais foi certificada a ocorrência de nematoides e realizadas medições de variáveis biofísicas e dados hiperespectrais na folha e sobre o dossel da planta. Os dados hiperespectrais também foram utilizados em simulação de bandas dos sensores do RapidEye e OLI/Landsat 8 para identificar as faixas espectrais mais sensíveis para a discriminação de patógenos em plantas de café. Nenhum dos parâmetros biofísicos avaliados discriminou eficientemente as folhas de plantas sadias e infectadas, mas a simulação de bandas indicou que os intervalos espectrais do vermelho, vermelho limítrofe e infravermelho próximos do RapidEye foram complementares para a discriminação de plantas de café sadio e dos dois níveis de infecção. Essas bandas, mais uma imagem NDVI, foram utilizadas na classificação das áreas infectadas por nematoides, a qual definiu a distribuição espacial de café sadio e dos dois níveis de infecção, com uma acurácia global de 78% e coeficiente kappa de 0,71. A classificação não supervisionada da imagem multiespectral OLI/Landsat 8 também definiu as três condições, porém com baixa confiabilidade (coeficiente kappa igual a 0,41). Por outro lado, uma inferência espacial quantitativa da concentração de nematoides/cm³ no solo, a partir de um modelo empírico baseado na imagem RapidEye, apresentou um erro consideravelmente alto (21,89%).
Nematodes are important phytoparasites that constitute a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study is to evaluate the potential of remote sensing data acquired in multiscale to discriminate and map healthy, early infected and severely infected coffee plants. This study was carried out in three experimental areas, located in the in southern Minas Gerais State, in which the occurrence of nematodes was certified and biophysical and hyperspectral measurements of the leaves and on the canopy were made. Hyperspectral data were also used to simulate the bands of the RapidEye and OLI/Landsat 8 sensors to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulations indicated that red, red edge and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. These bands, plus an (NDVI) image, were used for a multispectral classification of healthy and nematode-infected areas. The multispectral classification defined the spatial distribution of healthy, early infected and two levels of infection, with an overall accuracy of 78% and kappa coefficient of 0.71. The unsupervised classification of the multispectral image OLI/Landsat 8 also defined the three conditions, but with low reliability (kappa coefficient equal to 0.41). In contrast, a quantitative spatial inference of the soil nematode concentration/cm³, from an empirical model based on the RapidEye image, presented a considerably high error (21.89%).
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gomez, cecile. "Potentiels des données de télédétection multisources pour la cartographie géologique : Application à la région de Rehoboth (Namibie)." Phd thesis, Université Claude Bernard - Lyon I, 2004. http://tel.archives-ouvertes.fr/tel-00008556.
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Les données de télédétection dans le domaine du Visible, de l'Infrarouge et du rayonnement Gamma ont un potentiel pour la cartographie géologique. Ce potentiel est évalué sur l'exemple de la carte géologique de Rehoboth, zone semi aride de Namibie en contexte sédimentaire. Une méthode de détermination des contours géologiques a été mise au point à partir d'une combinaison de données multispectrales ASTER (3 bandes dans le Visible, 6 bandes dans l'Infrarouge Moyen), de données hyperspectrales HYPERION (242 bandes du visible à l'Infrarouge Moyen) et de données de rayonnement Gamma (K, U, Th). Cette méthode permet de préciser la géométrie de la carte. Le potentiel des données HYPERION a ensuite été évalué pour l'identification et la quantification des lithologies à partir de deux techniques : la méthode N-FindR et la méthode d'Analyse en Composantes Indépendantes (ACI). Les tests montrent que la méthode ACI permet d'identifier de façon plus fiable les composants lithologiques présents dans un pixel.
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Gomez, Cécile. "Potentiels des données de télédétection multisources pour la cartographie géologique : Application à la région de Rehoboth (Namibie)." Phd thesis, Université Claude Bernard - Lyon I, 2004. http://tel.archives-ouvertes.fr/tel-00665112.
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Les données de télédétection dans le domaine du Visible, de l'Infrarouge et du rayonnement Gamma ont un potentiel pour la cartographie géologique. Ce potentiel est évalué sur l'exemple de la carte géologique de Rehoboth, zone semi aride de Namibie en contexte sédimentaire. Une méthode de détermination des contours géologiques a été mise au point à partir d'une combinaison de données multispectrales ASTER (3 bandes dans le Visible, 6 bandes dans l'Infrarouge Moyen), de données hyperspectrales HYPERION (242 bandes du visible à l'Infrarouge Moyen) et de données de rayonnement Gamma (K, U, Th). Cette méthode permet de préciser la géométrie de la carte. Le potentiel des données HYPERION a ensuite été évalué pour l'identification et la quantification des lithologies ' partir de deux techniques : la méthode N-FindR et la méthode d'Analyse en Composantes Indépendantes (ACI). Les tests montrent que la méthode ACI permet d'identifier de façon plus fiable les composants lithologiques présents dans un pixel.
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Ahmad, Touseef. "Augmenting Hyperspectral Image Unmixing Models Using Spatial Correlation, Spectral Variability, And Sparsity." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6081.
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Hyperspectral imaging sensors sample sunlight reflected from different targets on Earth's surface by utilising a series of contiguous narrow spectral channels. The higher spectral resolution of hyperspectral images (HSIs) comes at the cost of low spatial resolution; therefore, most pixels may consist of multiple targets. Spectral unmixing algorithms are essential in addressing the issue of low spatial resolution of HSIs by incorporating spatial correlation, spectral variability, and sparsity constraints. Moreover, unmixing methods can be used to measure the fractional abundance of pure materials (called endmembers) in a mixed pixel and are also helpful in enhancing the spatial resolution of HSIs.
In the first part of the thesis, sparse unmixing methods were improved by incorporating high adjacency effects and endmember spectral variability. Traditional total-variation-based sparse unmixing methods avoid high adjacency effects among the neighbouring pixels, which leads to over-smoothing and causes errors in the abundance estimation. A four-directional total-variation spatial regularisation approach is proposed to address these issues, which yields robust results when applied to low signal-to-noise-ratio images. Furthermore, spectral unmixing algorithms analyse the HSI by treating endmembers as independent entities in many remote sensing applications such as agriculture or mineral study. Therefore, traditional methods fail to estimate the fractional abundance of endmembers accurately. An endmember variability-based spectral-spatial weighted sparse regression unmixing method is proposed and demonstrated using a real airborne AVIRIS-NG HSI over the agriculture field, where fractional covers of red and black soil were estimated over sparsely vegetated areas. The experimental finding shows promising results as compared to other methods.
In the second part, the generalised bilinear mixing (GBM) model-based nonlinear unmixing methods were improved. Real HSIs are usually contaminated with complex mixed noises such as Gaussian noise, dead pixels, stripes, impulse noise, etc. The intensity of mixed noise may also vary band-to-band in HSIs, which reduces the accuracy of traditional GBM-based unmixing methods. A computationally efficient bandwise-GBM model is proposed to deal with these issues. The proposed technique reduces computation time while being comparable (and often better) to traditional GBM-based unmixing methods.
Furthermore, traditional GBM-based unmixing approaches also reduce unmixing performance by ignoring spatial correlation among the neighbouring pixels. A super-pixel-guided weighted low-rank representation for the robust GBM model is proposed to overcome the above issues. This model employs an entropy rate superpixel segmentation approach to extract homogenous patches in the HSI that underlie the low-rank property. A weighted nuclear norm minimisation approach is introduced for each homogenous patch to estimate the low-rank property, which allocates smaller weights to larger singular values and higher weights to smaller ones. The proposed method significantly improves the fractional abundance estimation by incorporating spatial correlation and sparse noise constraints in the unmixing model.
Finally, spectral unmixing methods are utilised to improve the spatial resolution of HSI by employing high spatial resolution multispectral images (MSIs). Traditional unmixing-based fusion methods avoid noise effects in the modelling, which reduces the accuracy of fusion products. A robust coupled non-negative matrix factorisation is developed for HSI and MSI fusion, incorporating sparse noise effects in the unmixing models of HSI and MSI. Both unmixing problems are coupled by using the sensors' relative spectral response and point spread function.
The above study indicates that the proposed methods achieve robust performance by comprising spatial correlation, spectral variability, and sparsity constraints in the unmixing process.
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Chen, Yang-Chi 1973. "Knowledge-based learning for classification of hyperspectral data." 2007. http://hdl.handle.net/2152/15971.
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Mehta, Viraj Kirankumar. "Data fusion of multispectral remote sensing measurements using wavelet transform." 2003. http://www.lib.ncsu.edu/theses/available/etd-03282003-133133/unrestricted/etd.pdf.
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Unni, V. S. "Efficient and Convergent Algorithms for High-Fidelity Hyperspectral Image Fusion." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5916.
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Hyperspectral (HS) imaging refers to acquiring images with hundreds of bands corresponding to different wavelengths of light. HS imaging has a wide range of applications such as remote sensing, industrial inspection, environmental monitoring, etc. A fundamental consideration with multiband sensors is that the amount of incident energy is limited and this creates an intrinsic tradeoff between spatial resolution and the number of bands---current optical sensors can either generate images with high resolution but a small number of bands or images with a large number of bands but reduced resolution. For example, HS images have hundreds of bands but low spatial resolution, whereas the opposite is true for multispectral (MS) images. An extreme case is a panchromatic (PAN) image with very high spatial resolution but just a single band. Image fusion refers to techniques where multiband images with high spatial resolution are synthetically generated using image processing algorithms. It includes pansharpening (MS+PAN), hyperspectral sharpening (HS+PAN), and HS-MS fusion (HS+MS). Reconstructing a fused image from the observed images is ill-posed and needs regularization. Diverse regularization methods have been proposed over the years for general imaging problems, many of which perform very well for fusion. This includes vector total variation, sparsity and dictionary-based penalties, generalized Gaussian- and GMM-based priors, etc. This thesis proposes novel regularization models and algorithms that can outperform state-of-the-art image fusion techniques. We can broadly group these into two classes---explicit and implicit regularization.
Explicit regularization refers to the design of hand-crafted penalty functions that impose desirable properties (e.g., smoothness) on the reconstruction; this is used along with the observed data for fusion. We propose a convex regularizer that is motivated by nonlocal patch-based methods for image restoration. Our regularizer accounts for long-distance correlations in hyperspectral images, considers patch variation for capturing texture information, and uses the higher resolution image for guiding the fusion process. Unlike local pixel-based methods, where variations along just horizontal and vertical directions are penalized, we use a wider search window in terms of nonlocality and directionality. This is shown to yield state-of-the-art results. The catch is that the resulting optimization problem is non-differentiable and we cannot use simple gradient-based algorithms. However, we show that by expressing patch variation as filtering operations and judiciously splitting the original variables and introducing latent variables, we develop a provably convergent iterative algorithm, where the subproblems can be solved efficiently using FFT-based convolution and soft-thresholding.
In the implicit approach, we rely on a recent paradigm known as plug-and-play (PnP) regularization, where powerful off-the-shelf denoisers are used for regularization purposes. While this has been shown to give state-of-the-art results for general restoration tasks, it has not so much been explored for fusion. In fact, we faced few technical challenges in applying PnP for hyperspectral fusion. Firstly, existing denoisers are slow when applied to multiband images and we need to apply such denoisers several times with the PnP framework. Secondly, convergence is generally not guaranteed for PnP regularization since the mechanism is ad-hoc. Along with efficiency and good denoising performance, we need to come up with a denoiser with specific properties that can guarantee convergence. We proposed a couple of approaches to solve this problem. In the first approach, we have developed a high-dimensional kernel denoiser with low cost yet good denoising performance, which can guarantee PnP convergence. The overall algorithm is fast and competitive with state-of-the-art methods. In the second approach, we leverage the power of deep learning to develop a trained patch denoiser which has a couple of advantages over conventional end-to-end learning:
(1) Unlike end-to-end networks which require excessive ground-truth data for training, we can be trained the denoiser from patches extracted from the observed images. For example, in HS+MS fusion, the MS image captures the same scene and has the same spatial resolution as the target image. We train the denoiser by sampling clean patches from the MS image and corrupting them with noise.
(2) Compared to end-to-end learning, where the training is done with a fixed forward model, our method can be deployed for different forward models. This is possible thanks to the decoupling of the inversion (of the forward model) and denoising steps in PnP.
We use the trained denoiser for PnP regularization and establish convergence of the PnP iterations under a technical assumption that we verify numerically. As far as the reconstruction quality is concerned, our method outperforms state-of-the-art variational and deep-learning fusion techniques.
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Shieh, Chia-Sheng, and 謝嘉聲. "A Study on the Data Fusion for SPOT Multispectral and anchromatic Imagery." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/50277785057887986523.
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碩士國立交通大學
土木工程研究所
82
The integration of differebt data sorce inGIS has received increasing emphasis as a result of new development in computer and GIS techgnology. Integration of various data sets to fully utilize complementary information hsa become an important component. In recent years, there has been a large increase in the amount of image data that are available to users for various purposes. The launch of the French SPOT satellite system has given the capability to a range of land use and land cover analyses. This thesis compares the results of diffenent methods used to integrate the information contents of the SPOT Panchromatic and Multispectral image data. Four integrating methods, namely, clour space transformation, principal component analysis, high pass filter and radiometric method are ivvestigated. To evaluate the result of different merging methods, six measures are used. They are visual inspection, correlation, root mean square error, difference of two images, entropy value and the histogram comparison.
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Ferraz, Óscar Almeida. "Combining low-power with parallel processing for multispectral and hyperspectral image compression." Master's thesis, 2019. http://hdl.handle.net/10316/88005.
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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e TecnologiaO CCSDS 123 é um algoritmo de compressão de imagens hiperespectrais e multiespectrais composto por um preditor e um codificador. Normalmente, os sistemas que geram este tipo de imagens (satélites, drones, etc…) têm restrições energéticas. Este algoritmo é implementado, sobretudo em FPGAs devido ao seu baixo consumo energético. O mercado dos smartphones tem tornado os CPUs e GPUs em dispositivos energeticamente eficientes, colocando-os em posição de competir contra as FPGAs no campo de compressão de baixo consumo.O objetivo desta dissertação é, utilizando uma Jetson TX2, paralelizar o CCSDS-123. No preditor, quando a predição é intra-banda (P=0), é utilizado um único kernel. Quando se usa predição inter-banda (P>0), o preditor passa a ter dependências de dados dentro das bandas, tornando a paralelização menos eficiente e mais difícil de implementar. No codificador, que contém dependências de dados, são estudadas paralelizações utilizando vários dispositivos (CPU+GPU) nos dois codificadores contemplados nesta norma. Produzindo uma solução híbrida de computação heterogénea.As implementações são alvo de testes que compararam o tempo de execução paralela com os tempos execução em série de forma a identificar as melhores implementações. Ainda é feita uma análise energética medindo a potência utilizada pela placa ao longo do tempo de execução do algoritmo. No final, a taxa de débito e a eficiência energética são comparadas com o estado de arte.O uso de GPUs de baixo consumo traz um novo paradigma ao campo de compressão multiespectral e hiperespectral. Apesar de não tão eficientes como as FPGAs, GPUs conseguem altas taxas de débito.
The CCSDS 123 is a hyperspectral and multispectral image compression algorithm composed of a predictor and an encoder. Usually, the systems that generate these types of images (satellites, drones, etc.) have energy restrictions. Hence, FPGAs show themselves as efficient devices to implement the CCSDS 123 due to its low energy consumption. The smartphone market has turned CPUs and GPUs into energy-efficient systems, making them potential competitors against FPGAs implementation dominance in the field of low-energy compression.The objective of this dissertation is, using a low-power GPU (Jetson TX2), to parallelize the CCSDS 123. Intra-band prediction (P=0) uses a single kernel. When using inter-band prediction (P>0), the predictor has data dependencies within bands, making parallelization less efficient and more challenging to implement. Hybrid parallelizations (CPU+GPU) are studied for the two encoders designed for this standard, producing a heterogeneous computing system.The implementations are subject to tests that compare the parallel execution times with the serial execution times in order to identify the best implementations. An energy analysis is performed, measuring the power used by the board over the algorithm's running time. In the end, the throughput rate and energy efficiency are compared with the state-of-the-art.The use of low-power graphics processing units (GPUs) brings a new paradigm to the field of multispectral and hyperspectral compression. Even though, not as the efficiency as FPGAs, GPUs deliver high throughput rates.
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Arienzo, Alberto. "Multi-sensor Model-based Data Fusion for Remote Sensing Applications." Doctoral thesis, 2022. http://hdl.handle.net/2158/1272763.
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The thesis addresses a widespread topic of remote sensing, namely pansharpening, representing a specific instance of image fusion, where a panchromatic image, characterized by high spatial resolution and no spectral information, is pixelwise merged with a set of multispectral images, featuring complementary characteristics, i.e., lower spatial resolution and spectral diversity. Thus, the aim of pansharpening is to generate a final image product featuring the spatial information of the panchromatic and the spectral content of the multispectral data. The first contribution of the thesis is to provide a twofold representation of the state of the art of pansharpening: one from a fusion methodology perspective and one from a quality assessment standpoint. Initially, we present a review of the most widespread fusion techniques and algorithms, with particular attention to the following major categories: Component Substitution, Multi-Resolution Analysis, Variational Optimization, and Machine Learning. Furthermore, several state-of-the-art hybrid approaches, involving any combinations of the former categories, are also described. Thereafter, we introduce a second review of the most popular quality evaluation protocols, both at full and reduced resolutions, proposed over the years in the corresponding literature. The second contribution of the thesis is to present an investigation on the data-format reproducibility of pansharpening, both in terms of fusion and quality assessment. The aim of this study is to demonstrate whether the pansharpening process is influenced by the particular data-format over which the input imagery is represented, such as digital number, spectral radiance and spectral reflectance. It will be theoretically proven and experimentally demonstrated that Multi-Resolution Analysis methods are unaffected by the format of the data, which is not always true for Component Substitution methods; for the latter, only the employment of regression-based solutions allows to reach data-format reproducibility. On the quality assessment, it will be demonstrated that purely spectral indexes, such as the Spectral Angle Mapper, feature a significant data-format dependence, whereas for indexes balancing the spectral and radiometric similarity, like those based on hypercomplex numbers, i.e., Q2n, such a dependence weakens and completely vanishes for purely radiometric indexes, such as those based on error summation, e.g., Relative Dimensionless Global Error in Synthesis. The third and final contribution of the thesis is to provide a critical comparison of the most widespread full-resolution quality assessment protocols, such as the quality-with-no-reference, QNR, and its more recent variations, a.k.a QNR-like. Specifically, we present a thorough discussion of the pros and cons of each protocol, aimed at identifying strengths and weaknesses in order to support future research developments. In addition, the problem of the combination of the two spatio-spectral distortion indexes forming the general QNR-like index, is also addressed, by studying and testing solutions based on coefficient estimation instead of exploiting coefficients that are fixed to a constant value. Experiments both at reduced and full resolutions, comprising a wide qualitative analysis, are considered to support the statements on the QNR-like protocols. The study highlights the interesting features of the Filter-based QNR protocol and the spatial distortion index of the Regression-based QNR, thus suggesting the use of these complementary quality assessment measures to provide a comprehensive and consistent assessment at full resolution.
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"Discriminating wetland vegetation species in an African savanna using hyperspectral data." Thesis, 2010. http://hdl.handle.net/10413/2140.
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Wetland vegetation is of fundamental ecological importance and is used as one of the vital bio-indicators for early signs of physical or chemical degradation in wetland systems. Wetland vegetation is being threatened by expansion of extensive lowland areas of agriculture, natural resource exploitation, etc. These threats are increasing the demand for detailed information on vegetation status, up-to-date maps as well as accurate information for mitigation and adaptive management to preserve wetland vegetation. All these requirements are difficult to produce at species or community level, due to the fact that some parts of the wetlands are inaccessible. Remote sensing offers nondestructive and real time information for sustainable and effective management of wetland vegetation. The application of remote sensing in wetland mapping has been done extensively, but unfortunately the uses of narrowband hyperspectral data remain unexplored at an advanced level. The aim of this study is to explore the potential of hyperspectral remote sensing for wetland vegetation discrimination at species level. In particular, the study concentrates on enhancing or improving class separability among wetland vegetation species. Therefore, the study relies on the following two factors; a) the use of narrowband hyperspectral remote sensing, and b) the integration of vegetation properties and vegetation indices to improve accuracy. The potential of vegetation indices and red edge position were evaluated for vegetation species discrimination. Oneway ANOVA and Canonical variate analysis were used to statistically test if the species were significantly different and to discriminate among them. The canonical structure matrix revealed that hyperspectral data transforms can discriminate vegetation species with an overall accuracy around 87%. The addition of biomass and water content variables improved the accuracy to 95.5%. Overall, the study demonstrated that hyperspectral data and vegetation properties improve wetland vegetation separability at species level.Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.
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Kandare, Kaja. "Fusion of airborne laser scanning and hyperspectral data for predicting forest characteristics at different spatial scales." Doctoral thesis, 2017. http://hdl.handle.net/10449/44160.
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Forests can be characterized by many attributes such as mean height, volume, diameter at breast height (DBH), age, tree species distribution, and different indices describing productivity and diversity. All these characteristics can be estimated using a wide range of remote sensing data from aerial photography and airborne laser scanning (ALS) to spaceborne or airborne multispectral or hyperspectral sensors, etc. Remote sensing is a science to obtain information of objects without making any physical contact with it, typically from aircraft or satellites. In particular, this thesis focused on two remotely sensed data sources that at the moment seem to be the most promising for abovementioned purposes: ALS and airborne hyperspectral data. Their combined use or fusion can be beneficial as they provide a complementary information for characterizing forest attributes. ALS and hyperspectral technologies provide very high spatial resolution allowing us to map the forest attributes at a very high spatial detail. This can be useful for certain applications but increasing the spatial detail does not always improve the accuracy of the predictions. Indeed, many predicted forest characteristics can be explored at many spatial scales, e.g. from tree to stand. Thus, the major objective of this thesis was to evaluate the potential of fusing ALS and hyperspectral data for the prediction of forest characteristics and to evaluate the benefits of different spatial details in the prediction of such characteristics. The fusion of ALS and hyperspectral data and the spatial scale exploration were carried out simultaneously in this thesis, and in particular it started with a focus on the spatial scale (development of a new ITC delineation algorithm) and it finished with a focus only on data fusion (prediction of forest structural diversity measures).
The ALS and hyperspectral data were fused at two different levels, product and variable-level fusion. The product-level fusion was used for the prediction of the site index and species-specific volume, while the variable-level fusion was used for total and species-specific volume, as well as structural diversity measures. For the evaluation of different spatial details in the prediction of forest characteristics we considered three remotely sensed-based inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). In order to apply the ITC and semi-ITC approaches, the individual tree delineation algorithm was needed and developed based on the ALS point cloud. The forest characteristics evaluated in this thesis were: individual tree attributes (such as tree height, DBH, stem volume, age, and species), forest attributes (such as site index, total and species-specific volume), and forest structural diversity measures.
The ITC approach allowed an accurate determination of the height, species, DBH, and stem volume, while the age was subject to a greater error. The ITC approach for site index determination in combination with ALS and hyperspectral data was found to be an efficient and a stable procedure and it reached similar accuracy as in the existing site index maps based on field surveys. For species-specific volume, the ITC approach reached high accuracies but there were also large systematic errors for minority species. For majority species, the semi-ITC approach resulted in slightly higher accuracies and smaller systematic errors compared to ABA. In all three approaches, ALS and hyperspectral data were important to provide higher accuracies. The fusion of ALS and hyperspectral data for forest structural diversity measures did not improve their accuracy but produced accuracy levels comparable to the models built on ALS data alone, except for one measure. In these experiments, ALS data showed the best predictions for the majority of the structural diversity measures taken into account.
To conclude, the ITC and semi-ITC approaches can provide higher spatial detail of the predicted forest characteristics. This information can also be aggregated to coarser scales, e.g. stands. The use of ITC and semi-ITC approaches has a potential in different forestry and ecology applications, where the accuracy of the semi-ITC also showed the capacity in operational forest applications. The fusion of ALS and hyperspectral data improves the predictions of forest characteristics, such as volumes and site index, while for some forest structural diversity measures the fusion did not improve the accuracy of results. Fusion of such data, especially for structural diversity measures has to be further explored.
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Cheng, Jing-Yi, and 陳靖怡. "A Band Selection approach of Simulated Annealing Feature Uniformity for the Data Fusion of Hyperspectral and SAR Imageries." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/uv3k28.
Full textAbstract:
碩士國立臺北科技大學
電機工程系研究所
96
With the recent advances of state-of-the-art sensors, data initially developed in a few multispectral bands today can be now collected from several hundred hyperspectral and even thousands of ultraspectral bands. While images are continuously being acquired and archived, existing methodologies have proved inadequate for analyzing such large volumes of data. As a result, a vital demand exists for new concepts and methods to deal with high-dimensional datasets. In this paper,we fuse hyperspectral imaging and synthetic aperture radar imaging. We use Simulated annealing feature uniformity band selection (SAFU) from hyperspectral imaging feature extraction. Previously, scholars have put forward the “simulated annealing band selection” (SABS) . In this paper, we propose a novel feature extraction method, called simulated annealing feature uniformity (SAFU) band selection approach to improve the computational and the precise performances of the “clustered eigenspace / feature scale uniformity transformation” (CE/FSUT) of SABS method for clustering the CE features. It takes advantage of the special characteristics of SA to concentrate the CE feature sets of different classes into the most common feature subspaces. A distance measure based on SAFU is then applied to decompose the similarity for land cover classification purposes. Compared with the CE/FSUT method, the SAFU can group the CE feature sets of each different class in the same orders and can unify the feature scales of each different CE feature set at the same time. It can simultaneously group highly correlated bands of each different class into the same CE feature sets with higher effectiveness but lower computational loads. To demonstrate the advantages of the proposed method, we compared several different configurations categorized by the parameters of constructing SA annealing schedule. The performance of the propose method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the Airborne Synthetic Aperture Radar (AIRSAR) images. Compared with conventional feature extraction techniques, SAFU evinced improved discriminatory properties, crucial to subsequent PBF classification. It made use of the potentially significant separability embedded in high-dimensional datasets to select a unique set of the most important feature bands. The experimental results showed that the proposed SAFU approach is effective and can be used as an alternative to the existing feature extraction method for the data fusion of hyperspectral data sets.
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Liu, Jin-Nan, and 劉進男. "A Parallel Simulated Annealing Approach to Band Selection and Feature Extraction for the Data Fusion of Hyperspectral and SAR Imageries." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/kkg444.
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碩士國立臺北科技大學
電機工程系研究所
96
Satellite remote sensing images can interpret all kinds of large-scale terrain to understand the different topography of the distribution, such as sugarcane, oceans, paddy fields, reservoirs, and so on. Recent advances of satellite sensors technologies continue to update the development resulting in the increase of large spectral information available. The noises contained in these huge information can’t avoid the curse of dimensionality., As a result, how to efficiently select the right and effective spectral information has become important. This paper presents an alternative promising concept, known as the parallel simulated annealing band selection (PSABS), which adopts a novel parallel approach to the data fusion of remote sensing images of the same scene collected from multiple sources. The applications can be divided into three parts: 1.) a parallel simulated annealing (PSA), 2.) a clustered eigenspace / feature scale uniformity transformation (CE/FSUT), and a parallel positive Boolean function (PPBF). PSA and CE/FSUT are used to select the high-dimensional fused datasets, and cluster the highly related information to a set of modular subspaces. Finally, a PPBF classifer is then applied to these selected band modules to execute the classification. The effectiveness of the proposed PSABS is evaluated by MODIS/ASTER airborne simulator (MASTER) hyperspectral and SAR images for hyperspectral band selection. The experimental results demonstrated that PSABS can significantly improve the computational loads and provide a more reliable quality of solution compared to the traditional methods.
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(8713962), James Ulcickas. "LIGHT AND CHEMISTRY AT THE INTERFACE OF THEORY AND EXPERIMENT." Thesis, 2020.
Find full textAbstract:
Optics are a powerful probe of chemical structure that can often be linked to theoretical predictions, providing robustness as a measurement tool. Not only do optical interactions like second harmonic generation (SHG), single and two-photon excited fluorescence (TPEF), and infrared absorption provide chemical specificity at the molecular and macromolecular scale, but the ability to image enables mapping heterogeneous behavior across complex systems such as biological tissue. This thesis will discuss nonlinear and linear optics, leveraging theoretical predictions to provide frameworks for interpreting analytical measurement. In turn, the causal mechanistic understanding provided by these frameworks will enable structurally specific quantitative tools with a special emphasis on application in biological imaging. The thesis will begin with an introduction to 2nd order nonlinear optics and the polarization analysis thereof, covering both the Jones framework for polarization analysis and the design of experiment. Novel experimental architectures aimed at reducing 1/f noise in polarization analysis will be discussed, leveraging both rapid modulation in time through electro-optic modulators (Chapter 2), as well as fixed-optic spatial modulation approaches (Chapter 3). In addition, challenges in polarization-dependent imaging within turbid systems will be addressed with the discussion of a theoretical framework to model SHG occurring from unpolarized light (Chapter 4). The application of this framework to thick tissue imaging for analysis of collagen local structure can provide a method for characterizing changes in tissue morphology associated with some common cancers (Chapter 5). In addition to discussion of nonlinear optical phenomena, a novel mechanism for electric dipole allowed fluorescence-detected circular dichroism will be introduced (Chapter 6). Tackling challenges associated with label-free chemically specific imaging, the construction of a novel infrared hyperspectral microscope for chemical classification in complex mixtures will be presented (Chapter 7). The thesis will conclude with a discussion of the inherent disadvantages in taking the traditional paradigm of modeling and measuring chemistry separately and provide the multi-agent consensus equilibrium (MACE) framework as an alternative to the classic meet-in-the-middle approach (Chapter 8). Spanning topics from pure theoretical descriptions of light-matter interaction to full experimental work, this thesis aims to unify these two fronts.