Rozprawy doktorskie: „Remote Sensing Image Data Analysis”

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Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.

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Cheriyadat, Anil Meerasa. "Limitations of principal component analysis for dimensionality-reduction for classification of hyperspectral data". Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-11072003-133109.

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Robert, Denis J. "Selection and analysis of optimal textural features for accurate classification of monochrome digitized image data /". Online version of thesis, 1989. http://hdl.handle.net/1850/11364.

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KHALIQ, ALEEM. "Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture". Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2839838.

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Marcellin, Michael W., Naoufal Amrani, Serra-Sagristà Joan, Valero Laparra i Jesus Malo. "Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data". IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/621311.

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A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelettransformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

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Fischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher i Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application". WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.

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This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience

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Linden, Sebastian van der. "Investigating the potential of hyperspectral remote sensing data for the analysis of urban imperviousness". Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15757.

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Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt. Eine detaillierte Ergebnisvalidierung erfolgt mittels vielfältiger Referenzdaten. Es wird gezeigt, dass die Kartengenauigkeit von allen Verarbeitungsschritten abhängt: Support Vector Maschinen klassifizieren Hyperspektraldaten akkurat aber die Kartengenauigkeit wird durch die Georeferenzierung deutlich gemindert; die Versiegelungskartierung stellt die Situation am Boden gut dar, aber die Überdeckung versiegelter Flächen durch Bäume bedingt systematische Fehlschätzungen; eine Bildsegmentierung führt zu keiner Verbesserung der Klassifikationsergebnisse, bietet jedoch eine sinnvolle Möglichkeit zur effektiveren Prozessierung durch Datenkomprimierung. Auf diesem Weg ermöglicht die vorliegende Arbeit Rückschlüsse zur Verlässlichkeit von Datenprodukten, die eine Ausweitung fernerkundlicher Analysen in weniger gut dokumentierte urbane Räume voranbringt.
Urbanization is one of the most powerful and irreversible processes by which humans modify the Earth''s surface. Optical remote sensing is a main source of Earth observation products which help to better understand this dynamic process and its consequences. This work investigates the potential of airborne hyperspectral data to provide information on urban imperviousness that is needed for an integrated analysis of the coupled natural and human systems therein. For this purpose the complete processing workflow from preprocessing of the raw image to the generation of geocoded maps on land cover and impervious surface coverage is performed using Hyperspectral Mapper data acquired over Berlin, Germany. The traditional workflow for hyperspectral data is extended or modified at several points: a normalization of brightness gradients that are caused by directional reflectance properties of urban surfaces is included into radiometric preprocessing; support vector machines are used to classify five spectrally complex land cover classes without previous feature extraction or the definition of sub-classes. A detailed assessment of such maps is performed based on various reference products. Results show that the accuracy of derived maps depends on several steps within the processing workflow. For example, the support vector machine classification of hyperspectral data itself is accurate but geocoding without detailed terrain information introduces critical errors; impervious surface estimates correlate well with ground data but trees covering impervious surface below generally causes offsets; image segmentation does not enhance spectral classification accuracy of the spatially heterogeneous area but offers an interesting way of data compression and more time effective processing. Findings from this work help judging the reliability of data products and in doing so advance a possible extension of urban remote sensing approaches to areas where only little additional data exists.

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Parshakov, Ilia. "Automatic class labeling of classified imagery using a hyperspectral library". Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2012, 2012. http://hdl.handle.net/10133/3372.

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Image classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with.
vii, 93 leaves : ill., maps (some col.) ; 29 cm

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Magnini, Luigi. "Remote sensing e object-based image analysis: metodologie di approccio per la creazione di standard archeologici". Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3423260.

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In recent years, the field of remote sensing experienced an incredible growth thanks to the increasing quality and variety of sensors and the reduction of instrumental costs. The benefits for archaeology were soon apparent. So far, data interpretation remains essentially a prerogative of the human operator and is mediated by his skills and experiences. The continuous increase of datasets volume, i.e. the Big Data Explosion, and the increasing necessity to work on large scale projects require an overall revision of the methods traditionally used in archeology. In this sense, the research presented hereinafter contributes to assess the limits and potential of the emerging field of object-based image analysis (OBIA). The work focused on the definition of OBIA protocols for the treatment of three-dimensional data acquired by airborne and terrestrial laser scanning through the development of a wide range of case studies, used to illustrate the possibilities of the method in archeology. The results include a new, automated approach to identify, map and quantify traces of the First World War landscape around Fort Lusern (Province of Trento, Italy) and the recalcified osteological tissue on the skulls of two burials in the protohistoric necropolis of Olmo di Nogara (Province of Verona, Italy). Moreover, the method was employed to create a predictive model to locate “control places” in mountainous environments; the simulation was built for the Western Asiago Plateau (Province of Vicenza, Italy) and then re-applied with success in basin of Bressanone (Province of Bolzano, Italy). The accuracy of the results was verified thanks to respectively ground surveys, remote cross-validation and comparison with published literature. This confirmed the potential of the methodology, giving reasons to introduce the concept of Archaeological Object-Based Image Analysis (ArchaeOBIA), used to highlight the role of object-based applications in archaeology.
Il campo del remote sensing ha vissuto un incredibile sviluppo negli ultimi anni per merito della crescente qualità e varietà dei sensori e dell’abbattimento dei costi strumentali. Le potenzialità archeologiche sono state ben presto evidenti. Finora, l’interpretazione dei dati è rimasta però prerogativa dell’operatore umano, mediata dalle sue competenze e dalla sua esperienza. Il progressivo aumento di volume dei dataset (cd. “big data explosion”) e la necessità di lavorare su progetti territoriali ad ampia scala hanno reso ora indispensabile una revisione delle modalità di studio tradizionalmente impiegate in ambito archeologico. In questo senso, la ricerca presentata di seguito contribuisce alla valutazione delle potenzialità e dei limiti dell’emergente campo d’indagine dell’object-based image analysis (OBIA). Il lavoro si è focalizzato sulla definizione di protocolli OBIA per il trattamento di dati tridimensionali acquisiti tramite laser scanner aviotrasportato e terrestre attraverso l’elaborazione di un variegato spettro di casi di studio in grado di esemplificare le possibilità offerte dal metodo in archeologia. I risultati ottenuti hanno consentito di identificare, mappare e quantificare in modo automatico e semi-automatico le tracce del paesaggio di guerra nell’area intorno a Forte Luserna (TN) e il tessuto osteologico ricalcificato sui crani di due inumati della necropoli protostorica dell’Olmo di Nogara (VR). Infine, il metodo è stato impiegato per lo sviluppo di un modello predittivo per la localizzazione dei “punti di controllo” in ambiente montano, che è stato studiato per l’area occidentale dell’Altopiano di Asiago (VI) e in seguito riapplicato con successo nella conca di Bressanone (BZ). L’accuratezza dei risultati, verificati di volta in volta tramite ricognizioni a terra, validazione incrociata tramite analisi da remoto e comparazione con i dati editi in letteratura, ha confermato il potenziale della metodologia, consentendo di introdurre il concetto di Archaeological Object-Based Image Analysis (ArchaeOBIA), per rimarcare le specificità delle applicazioni object-based nell’ambito della disciplina archeologica.

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Gapper, Justin J. "Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images". Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_dissertations/2.

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This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a methodology for developing a robust machine learning classifier that can effectively be deployed to accurately detect coral reefs at scale. The hypothesis is that Landsat data can be used to train a classifier to detect coral reefs in remote sensing imagery and that this classifier can be trained to generalize across multiple sites. Another objective is to identify how well different classifiers perform under the generalized conditions and how unique the spectral signature of coral is as environmental conditions vary across observation sites. A methodology for validating the generalization performance of a classifier to unseen locations is proposed and implemented (Controlled Parameter Cross-Validation,). Analysis is performed using satellite imagery from nine different locations with known coral reefs (six Pacific Ocean sites and three Red Sea sites). Ground truth observations for four of the Pacific Ocean sites and two of the Red Sea sites were used to validate the proposed methodology. Within the Pacific Ocean sites, the consolidated classifier (trained on data from all sites) yielded an accuracy of 75.5% (0.778 AUC). Within the Red Sea sites, the consolidated classifier yielded an accuracy of 71.0% (0.7754 AUC). Finally, long-term change detection analysis is conducted for each of the sites evaluated. In total, over 16,700 km2 was analyzed for benthic cover type and cover change detection analysis. Within the Pacific Ocean sites, decreases in coral cover ranged from 25.3% reduction (Kingman Reef) to 42.7% reduction (Kiritimati Island). Within the Red Sea sites, decrease in coral cover ranged from 3.4% (Umluj) to 13.6% (Al Wajh).

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Yang, Hsien-Min 1957. "PRINCIPAL COMPONENTS AND TEXTURE ANALYSIS OF THE NS-001 THEMATIC MAPPER SIMULATOR DATA IN THE ROSEMONT MINING DISTRICT, ARIZONA (GEOLOGIC, DIGITAL IMAGE PROCESSING, TEXTURE EXTRACTION)". Thesis, The University of Arizona, 1985. http://hdl.handle.net/10150/275436.

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Richter, Nicole. "Pedogenic iron oxide determination of soil surfaces from laboratory spectroscopy and HyMap image data". Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2010. http://dx.doi.org/10.18452/16119.

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Zusammenfassung Kenntnisse über den Zustand und die Entwicklung von Böden sind entscheidend für die Charakterisierung von Ökosystemen und deren Veränderungen. Die weltweite Verbreitung von Eisenoxiden und ihre von der Bodenentwicklung abhängige Konzentration und mineralogische Zusammensetzung machen sie zu geeigneten Indikatoren. Methoden der optische Fernerkundung wurden angewandt, um am Beispiel des Cabo de Gata- Níjar Naturparks, einem semi-ariden Ökosystem in Südostspanien, die Konzentrationen von Eisenoxiden im Boden zu bestimmen und zu kartieren. In der zuerst durchgeführten laborspektroskopischen Studie wurde eine Methode entwickelt, welche den Eisenoxidgehalt (Fed, Citrat-Dithionit extrahierbares Eisenoxid) mit den Eisenabsorptionsbanden verknüpft. Korngrößenabhängige Fed Vorhersagemodelle wurden sowohl für sand- als auch ton-schluff-haltige Proben erstellt. Beide liefern hochgenaue Schätzungen mit weniger als 15% Vorhersagefehler. Ähnliche Werte wurden für korngrößenunabhängige Modelle erreicht. Korngrößenunabhängige Modelle wurden zur Analyse der HyMap-Bilddaten verwendet, da eine pixelbezogene Bestimmung der vorherrschenden Bodentextur nicht möglich war. Die räumliche Verteilung der Fed Konzentration im Untersuchungsgebiet wurde mit einer den Laborergebnissen vergleichbaren Genauigkeit bestimmt. Laboruntersuchungen zum Vegetationseinfluss in Bezug auf Vitalität und Bedeckungsgrad auf die Bodenreflektionsspektren und die Fed Vorhersagegenauigkeit zeigten, dass zuverlässige Abschätzungen bis zu einer Vegetationsbedeckung von ca. 20 % möglich sind. Dementsprechend wurden drei Vorhersagegenauigkeitsklassen definiert, basierend auf der gemeinsamen Detektierbarkeit von Vegetation und Eisenabsorptionsbanden im Bildpixel. Die abgeleitete Fed Verteilungskarte dient der Einschätzung des vorliegenden Bodenzustands und dem Ausweisen von erodierten Oberflächen. Die entwickelte Methode hat aufgrund ihrer Einfachheit ein großes Potential für ein globales Monitoring von sensitiven Gebieten unter der Verwendung von gegenwärtig verfügbaren als auch zukünftigen satellitengestützten Sensoren.
Abstract The knowledge of the soil condition and development is decisive when characterizing and monitoring the change of ecosystems. The global presence of iron oxides and their highly variable concentration and mineralogy reflecting different soil conditions make them a suitable indicator. Optical remote sensing methods are employed to determine and map the soil iron oxide concentrations on the example of the Cabo de Gata-Níjar Natural Park, a semi-arid ecosystem in SE Spain. In an initial laboratory spectroscopy study, a methodology is developed that links iron oxide content (Fed, citrate-dithionite extractable iron oxides) with iron spectral absorption bands. Texture-dependent Fed prediction models are developed for sand- and clay-silt-dominated samples. They yield highly accurate estimations with less than 15 % prediction error. Similar accuracies are achieved from texture-independent models. Texture-independent models are applied to the HyMap image data because a pixel-wise determination of the predominating soil texture is not possible. However, the spatial distribution of Fed concentration in the study area is determined with comparable accuracy as in the laboratory. Laboratory analysis of vegetation vitality and density impact on the soil reflectance spectra and Fed prediction accuracy has shown that reliable estimations are possible until about 20 % leaf cover. Accordingly, three Fed prediction accuracy levels are defined based on the joint detectability of vegetation and iron absorption features. The final Fed prediction map is used to evaluate the current soil conditions and identify potentially eroded soils surfaces. The present method has due to low complexity a high potential for the global monitoring of such sensitive areas from current and future spaceborne sensors.

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Yang, Bo. "Spatio-temporal Analysis of Urban Heat Island and Heat Wave Evolution using Time-series Remote Sensing Images: Method and Applications". University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552398782461458.

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Shah, Vijay Pravin. "A wavelet-based approach to primitive feature extraction, region-based segmentation, and identification for image information mining". Diss., Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-07062007-134150.

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Coladello, Leandro Fernandes. "Integration of heterogeneous data in time series : a study of the evolution of aquatic macrophytes in eutrophic reservoirs based on multispectral images and meteorological data /". Presidente Prudente, 2020. http://hdl.handle.net/11449/192672.

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Orientador: Maria de Lourdes Bueno Trindade Galo
Resumo: O represamento de rios para a produção de energia elétrica usualmente provoca atividades antrópicas que impactam um ecossistema aquático fortemente. Uma das consequências de se instalar pequenos reservatórios em regiões sujeitas à intensos processos de urbanização e industrialização é a abundância de macrófitas, resultante do despejo de nutrientes em grandes concentrações no ecossistema aquático. Recentemente, o grande volume de images multitemporais de sensoriamento remoto disponíveis em bancos de dados gratuitos, bem como a alta performance computacional que permite a mineração de grandes volumes de dados, fazem com que o monitoramento de fenômenos ambientais seja um objeto de estudo recorrente. O propósito desse estudo é desenvolver uma metodologia baseada na integração de dados heterogêneos, fornecidos por séries temporais de coleções de imagens multiespectrais e multitemporais Landsat e coleções de dados climáticos históricos, para investigar a evolução e comportamento espacial de macrófitas aquáticas em lagos e reservatórios eutrofizados. A extensa coleção temporal de imagens de superfície de reflectância Landsat disponível e também dados de variáveis ambientais permitiram a construção e análise de séries temporais para investigar a recorrente abundância de macrófitas no reservatório de Salto Grande, localizado na região metropolitana de Campinas, São Paulo, Brasil. Inicialmente, foi encontrado que as imagens Landsat possuem a qualidade radiométrica necessária para se r... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: River damming for electric power production usually triggers anthropic activities that strongly impact on aquatic ecosystem. One of the consequences of installing small reservoirs in regions subject to an intense process of urbanization and industrialization is the overabundance of macrophytes, resulting from the input of nutrients in high concentration into the aquatic ecosystem. Currently, the large volume of multitemporal remote sensing images available in open data sources, as well as the high computational performance that allow the mining of large volumes of data has made the monitoring of environmental phenomena a recurrent object of analysis. The aim of this study is to develop a methodology based on the integration of heterogeneous data, provided by time series of multispectral and multitemporal Landsat images and collections of historical climatic data, to investigate the evolution and spatial behavior of aquatic macrophytes in lakes and eutrophic reservoirs. So, the extensive temporal collection of the Landsat surface reflectance images made available as well as environmental variables data permitted the construction and analysis of time series to investigate the recurrent over-abundance of macrophytes in Salto Grande reservoir, located in the metropolitan region of Campinas, São Paulo, Brazil. Initially, it was found that the the Landsat images have the necessary radiometric quality to perform the time series analyses, through an assessment based on information ab... (Complete abstract click electronic access below)
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Evans, Ben Richard. "Data-driven prediction of saltmarsh morphodynamics". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276823.

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Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.

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Hoxha, Genc. "IMAGE CAPTIONING FOR REMOTE SENSING IMAGE ANALYSIS". Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/351752.

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Image Captioning (IC) aims to generate a coherent and comprehensive textual description that summarizes the complex content of an image. It is a combination of computer vision and natural language processing techniques to encode the visual features of an image and translate them into a sentence. In the context of remote sensing (RS) analysis, IC has been emerging as a new research area of high interest since it not only recognizes the objects within an image but also describes their attributes and relationships. In this thesis, we propose several IC methods for RS image analysis. We focus on the design of different approaches that take into consideration the peculiarity of RS images (e.g. spectral, temporal and spatial properties) and study the benefits of IC in challenging RS applications. In particular, we focus our attention on developing a new decoder which is based on support vector machines. Compared to the traditional decoders that are based on deep learning, the proposed decoder is particularly interesting for those situations in which only a few training samples are available to alleviate the problem of overfitting. The peculiarity of the proposed decoder is its simplicity and efficiency. It is composed of only one hyperparameter, does not require expensive power units and is very fast in terms of training and testing time making it suitable for real life applications. Despite the efforts made in developing reliable and accurate IC systems, the task is far for being solved. The generated descriptions are affected by several errors related to the attributes and the objects present in an RS scene. Once an error occurs, it is propagated through the recurrent layers of the decoders leading to inaccurate descriptions. To cope with this issue, we propose two post-processing techniques with the aim of improving the generated sentences by detecting and correcting the potential errors. They are based on Hidden Markov Model and Viterbi algorithm. The former aims to generate a set of possible states while the latter aims at finding the optimal sequence of states. The proposed post-processing techniques can be injected to any IC system at test time to improve the quality of the generated sentences. While all the captioning systems developed in the RS community are devoted to single and RGB images, we propose two captioning systems that can be applied to multitemporal and multispectral RS images. The proposed captioning systems are able at describing the changes occurred in a given geographical through time. We refer to this new paradigm of analysing multitemporal and multispectral images as change captioning (CC). To test the proposed CC systems, we construct two novel datasets composed of bitemporal RS images. The first one is composed of very high-resolution RGB images while the second one of medium resolution multispectral satellite images. To advance the task of CC, the constructed datasets are publically available in the following link: https://disi.unitn.it/~melgani/datasets.html. Finally, we analyse the potential of IC for content based image retrieval (CBIR) and show its applicability and advantages compared to the traditional techniques. Specifically, we focus our attention on developing a CBIR systems that represents an image with generated descriptions and uses sentence similarity to search and retrieve relevant RS images. Compare to traditional CBIR systems, the proposed system is able to search and retrieve images using either an image or a sentence as a query making it more comfortable for the end-users. The achieved results show the promising potentialities of our proposed methods compared to the baselines and state-of-the art methods.

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Jia, Xiuping Electrical Engineering Australian Defence Force Academy UNSW. "Classification techniques for hyperspectral remote sensing image data". Awarded by:University of New South Wales - Australian Defence Force Academy. School of Electrical Engineering, 1996. http://handle.unsw.edu.au/1959.4/38713.

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Hyperspectral remote sensing image data, such as that recorded by AVIRIS with 224 spectral bands, provides rich information on ground cover types. However, it presents new problems in machine assisted interpretation, mainly in long processing times and the difficulties of class training due to the low ratio of number of training samples to the number of bands. This thesis investigates feasible and efficient feature reduction and image classification techniques which are appropriate for hyperspectral image data. The study is reported in three parts. The first concerns a deterministic approach for hyperspectral data interpretation. Multigroup and multiple threshold spectral coding procedures, and associated techniques for spectral matching and classification, are proposed and tested. By coding on subgroups of bands using one or three thresholds, spectral searching and matching becomes simple, fast and free of the need for radiometric correction. Modifications of existing statistical techniques are proposed in the second part of the investigation A block-based maximum likelihood classification technique is developed. Several subgroups are formed from the complete set of spectral bands in the data, based on the properties of global correlation among the bands. Subgroups which are poorly correlated with each other are treated independently using conventional maximum likelihood classification. Experimental results demonstrate that, when using appropriate subgroup sizes, the new method provides a compromise among classification accuracy, processing time and available training pixels. Furthermore, a segmented, and possibly multi-layer, principal components transformation is proposed as a possible feature reduction technique prior to classification, and for effective colour display. The transformation is performed efficiently on each of the highly correlated subgroups of bands independently. Selected features from each transformed subgroup can be then transformed again to achieve a satisfactory data reduction ratio and to generate the three most significant components for colour display. Classification accuracy is improved and high quality colour image display is achieved in experiments using two AVIRIS data sets.

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Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis". Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.

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Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis". Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.

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21

Alam, Mohammad Tanveer. "Image Classification for Remote Sensing Using Data-Mining Techniques". Youngstown State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161.

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Garner, Jamada J. "Scene classification using high spatial resolution multispectral data". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FGarner.pdf.

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Slone, Ambrose J. (Abrose Jay). "Improved remote sensing data analysis using neural networks". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11461.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaf 115).
by Ambrose J. Slone.
M.Eng.

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24

Philipson, née Ammenberg Petra. "Environmental Applications of Aquatic Remote Sensing". Doctoral thesis, Uppsala University, Centre for Image Analysis, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3328.

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Many lakes, coastal zones and oceans are directly or indirectly influenced by human activities. Through the outlet of a vast amount of substances in the air and water, we are changing the natural conditions on local and global levels.

Remote sensing sensors, on satellites or airplanes, can collect image data, providing the user with information about the depicted area, object or phenomenon. Three different applications are discussed in this thesis. In the first part, we have used a bio-optical model to derive information about water quality parameters from remote sensing data collected over Swedish lakes. In the second part, remote sensing data have been used to locate and map wastewater plumes from pulp and paper industries along the east coast of Sweden. Finally, in the third part, we have investigated to what extent satellite data can be used to monitor coral reefs and detect coral bleaching.

Regardless of application, it is important to understand the limitations of this technique. The available sensors are different and limited in terms of their spatial, spectral, radiometric and temporal resolution. We are also limited with respect to the objects we are monitoring, as the concentration of some substances is too low or the objects are too small, to be identified from space. However, this technique gives us a possibility to monitor our environment, in this case the aquatic environment, with a superior spatial coverage. Other advantages with remote sensing are the possibility of getting updated information and that the data is collected and distributed in digital form and therefore can be processed using computers.

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25

Sarton, Christopher J. "Autopilot using differential thrust for ARIES autonomous underwater vehicle". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FSarton.pdf.

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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis". Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution images. However, they provide sub-optimal results on new generation images due to their limited capability of modeling complex spatial and spectral information in the new generation products. Although significant number of object-based methods have been proposed in the last decade, they depend on suitable segmentation scale for diverse kinds of objects present in each temporal image. Thus their capability to express contextual information is limited. Typical spatial properties of last generation images emphasize the need of having more flexible models for object representation. Another drawback of the traditional methods is the difficulty in transferring knowledge learned from one specific problem to another. In the last few years, an interesting development is observed in the machine learning/computer vision field. Deep learning, especially Convolution Neural Networks (CNNs) have shown excellent capability to capture object level information and in transfer learning. By 2015, deep learning achieved state-of-the-art performance in most computer vision tasks. Inspite of its success in computer vision fields, the application of deep learning in multi-temporal image analysis saw slow progress due to the requirement of large labeled datasets to train deep learning models. However, by the start of this PhD activity, few works in the computer vision literature showed that deep learning possesses capability of transfer learning and training without labeled data. Thus, inspired by the success of deep learning, this thesis focuses on developing deep learning based methods for unsupervised/semi-supervised multi-temporal image analysis. This thesis is aimed towards developing methods that combine the benefits of deep learning with the traditional methods of multi-temporal image analysis. Towards this direction, the thesis first explores the research challenges that incorporates deep learning into the popular unsupervised change detection (CD) method - Change Vector Analysis (CVA) and further investigates the possibility of using deep learning for multi-temporal information extraction. The thesis specifically: i) extends the paradigm of unsupervised CVA to novel Deep CVA (DCVA) by using a pre-trained network as deep feature extractor; ii) extends DCVA by exploiting Generative Adversarial Network (GAN) to remove necessity of having a pre-trained deep network; iii) revisits the problem of semi-supervised CD by exploiting Graph Convolutional Network (GCN) for label propagation from the labeled pixels to the unlabeled ones; and iv) extends the problem statement of semantic segmentation to multi-temporal domain via unsupervised deep clustering. The effectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving passive VHR (including Pleiades), passive HR (Sentinel-2), and active VHR (COSMO-SkyMed) datasets. A substantial improvement is observed over the state-of-the-art shallow methods.

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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis". Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution images. However, they provide sub-optimal results on new generation images due to their limited capability of modeling complex spatial and spectral information in the new generation products. Although significant number of object-based methods have been proposed in the last decade, they depend on suitable segmentation scale for diverse kinds of objects present in each temporal image. Thus their capability to express contextual information is limited. Typical spatial properties of last generation images emphasize the need of having more flexible models for object representation. Another drawback of the traditional methods is the difficulty in transferring knowledge learned from one specific problem to another. In the last few years, an interesting development is observed in the machine learning/computer vision field. Deep learning, especially Convolution Neural Networks (CNNs) have shown excellent capability to capture object level information and in transfer learning. By 2015, deep learning achieved state-of-the-art performance in most computer vision tasks. Inspite of its success in computer vision fields, the application of deep learning in multi-temporal image analysis saw slow progress due to the requirement of large labeled datasets to train deep learning models. However, by the start of this PhD activity, few works in the computer vision literature showed that deep learning possesses capability of transfer learning and training without labeled data. Thus, inspired by the success of deep learning, this thesis focuses on developing deep learning based methods for unsupervised/semi-supervised multi-temporal image analysis. This thesis is aimed towards developing methods that combine the benefits of deep learning with the traditional methods of multi-temporal image analysis. Towards this direction, the thesis first explores the research challenges that incorporates deep learning into the popular unsupervised change detection (CD) method - Change Vector Analysis (CVA) and further investigates the possibility of using deep learning for multi-temporal information extraction. The thesis specifically: i) extends the paradigm of unsupervised CVA to novel Deep CVA (DCVA) by using a pre-trained network as deep feature extractor; ii) extends DCVA by exploiting Generative Adversarial Network (GAN) to remove necessity of having a pre-trained deep network; iii) revisits the problem of semi-supervised CD by exploiting Graph Convolutional Network (GCN) for label propagation from the labeled pixels to the unlabeled ones; and iv) extends the problem statement of semantic segmentation to multi-temporal domain via unsupervised deep clustering. The effectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving passive VHR (including Pleiades), passive HR (Sentinel-2), and active VHR (COSMO-SkyMed) datasets. A substantial improvement is observed over the state-of-the-art shallow methods.

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28

Kressler, Florian. "The Integration of Remote Sensing and Ancillary Data". WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4256/1/WSG_RR_0896.pdf.

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Obtaining up-to-date information concernmg the environment at reasonable costs is a challenge faced by many institutions today. Satellite images meet both demands and thus present a very attractive source of information. The following thesis deals with the comparison of satellite images and a vector based land use data base of the City of Vienna. The satellite data is transformed using the spectral mixture analysis, which allows an investigation at a sub-pixel level. The results of the transformation are used to determine how suitable this spectral mixture analysis is to distinguish different land use classes in an urban area. In a next step the results of the spectral mixture analysis of two different images (recorded in 1986 and 1991) are used to undertake a change detection. The aim is to show those areas, where building activities have taken place. This information may aid the update of data bases, by limiting a detailed examination of an area to those areas, which show up as changes in the change detection. The proposed method is a fast and inexpensive way of analysing large areas and highlighting those areas where changes have taken place. lt is not limited to urban areas but may easily be adapted for different environments. (author's abstract)
Series: Research Reports of the Institute for Economic Geography and GIScience

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Humphrey, Matthew Donald. "Texture analysis of high resolution panchromatic imagery for terrain classification". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FHumphrey.pdf.

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Kumar, Mrityunjay. "Model based image fusion". Diss., Connect to online resource - MSU authorized users, 2008.

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Sun, Liqun, i 孙立群. "A comprehensive analysis of terrestrial surface features using remote sensing data". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208044.

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Using the remote sensing data, this study aims to enhance our understanding of land surface features, including ecosystem distribution in association with topographic controls and climatic controls, vegetation disturbance due to natural hazards, and surface temperature changes with consideration of the influence of urbanization. In this study, the Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets from 1982 to 2006 were used to explore vegetation variation. A data mining method, Exhaustive Chi-squared Automatic Interaction Detector algorithm, was successfully applied to investigate the topographic influences on vegetation distribution in China. The study revealed that elevation is a predominant factor for controlling vegetation distribution among different topographic attributes (slope, aspect, Compound Topographic Index (CTI) and distance to the nearest river). Further, the study results indicated that solar radiation is the limited factor for plant growth in majority of the Northern Hemisphere in summer, and temperature is the main limitation for other seasons. Partial correlation coefficient (PCC) method was adopted to investigate the complex relationships of NDVI with weather variables (i.e., temperature, precipitation and solar radiation) and key climate indices (such as, El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Arctic Oscillation (AO), and Antarctic Oscillation (AAO)). The study indicated that AO is the most significant index in affecting the temperatures in spring and winter in the Northern Hemisphere. This study enhanced the understanding of vegetation responds to asymmetric daytime (Tmax) and nighttime (Tmin) warming in different seasons. The result revealed that asymmetric warming of Tmax and Tmin may influence vegetation photosynthesis and respiration in the plant growth in different periods across biomes. In spring and autumn, vegetation in boreal and wet temperate regions of the Northern Hemisphere is positively correlated with Tmax and negatively correlated with Tmin, whereas, in dry regions the NDVI is always negatively correlated with Tmax and positively correlated with Tmin. In summer, the NDVI is negatively correlated with Tmax in many dry regions. In addition, this study developed a new index, Continued Vegetation Decrease Index (CVDI), to detect vegetation disturbance due to extreme natural hazards (such as, earthquake, wildfire, ice storms and so on). Using the Wenchuan earthquake occurred in Sichuan China on 12 May 2008 as an example, this study confirmed that the CVDI method can effectively identify the regions with severe vegetation damage, and it is expected that the newly-developed index can be used for detecting vegetation disturbance in other regions of the world. Finally, using the remote sensing data (land use data and surface temperature data) and weather station data, this study developed a new method to evaluate the urbanization influence on the temperature recorded at weather stations. The results revealed that the weather stations with most fast increase temperature are not in developed countries, but in developing countries. The results also imply that the global warming trend may be overestimated due to the under-estimation of urbanization influence on temperature increase.
published_or_final_version
Civil Engineering
Doctoral
Doctor of Philosophy

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32

Combrexelle, Sébastien. "Multifractal analysis for multivariate data with application to remote sensing". Phd thesis, Toulouse, INPT, 2016. http://oatao.univ-toulouse.fr/16477/1/Combrexelle.pdf.

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Texture characterization is a central element in many image processing applications. Texture analysis can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the coefficients or wavelet leaders. Although successfully applied in various contexts, multi fractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small sample sizes. Second, multifractal analysis has so far been limited to the analysis of a single image, while the data available in applications are increasingly multivariate. The main goal of this thesis is to develop practical contributions to overcome these limitations. The first limitation is tackled by introducing a generic statistical model for the logarithm of wavelet leaders, parametrized by multifractal parameters of interest. This statistical model enables us to counterbalance the variability induced by small sample sizes and to embed the estimation in a Bayesian framework. This yields robust and accurate estimation procedures, effective both for small and large images. The multifractal analysis of multivariate images is then addressed by generalizing this Bayesian framework to hierarchical models able to account for the assumption that multifractal properties evolve smoothly in the dataset. This is achieved via the design of suitable priors relating the dynamical properties of the multifractal parameters of the different components composing the dataset. Different priors are investigated and compared in this thesis by means of numerical simulations conducted on synthetic multivariate multifractal images. This work is further completed by the investigation of the potential benefit of multifractal analysis and the proposed Bayesian methodology for remote sensing via the example of hyperspectral imaging.

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Castelletti, Davide. "Advanced regression and detection methods for remote sensing data analysis". Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368526.

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Nowadays the analysis of remote sensing data for environmental monitoring is fundamental to understand the local and global Earth dynamics. In this context, the main goal of this thesis is to present novel signal processing methods for the estimation of biophysical parameters and for the analysis icy terrain with active sensors. The thesis presents three main contributions. In the context of biophysical parameters estimation we focus on regression methods. According to the analysis of the literature, most of the regression techniques require a relevant number of reference samples to model a robust regression function. However, in real-word applications the ground truth observations are limited as their collection leads to high operational cost. Moreover, the availability of biased samples may result in low estimation accuracy. To address these issues, in this thesis we propose two novel contributions. The first contribution is a method for the estimation of biophysical parameters that integrates theoretical models with empirical observations associated to a small number of in-situ reference samples. The proposed method computes and correct deviations between estimates obtained through the inversion of theoretical models and empirical observations. The second contribution is a semisupervised learning (SSL) method for regression defined in the context of the Îµ-insensitive SVR. The proposed SSL method aims to mitigate the problems of small-sized biased training sets by injecting priors information in the initial learning of the SVR function, and jointly exploiting labeled and unlabeled samples in the learning phase of the SVR. The third contribution of this dissertation addresses the clutter detection problem in radar sounder (RS) data. The capability to detect clutter is fundamental for the interpretation of subsurface features in the radargram. In the state of the art, techniques that require accurate information on the surface topography or approaches that exploit complex multi-channel radar sounder systems have been presented. In this thesis, we propose a novel method for clutter detection that is independent from ancillary information and limits the hardware complexity of the radar system. The method relies on the interferometric analysis of two-channel RS data and discriminates the clutter and subsurface echoes by modeling the theoretical phase difference between the cross-track antennas of the RS. This allows the comparison of the phase difference distributions of real and simulated data. Qualitative and quantitative experimental results obtained on real airborne SAR and RS data confirm the effectiveness of the proposed methods.

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Castelletti, Davide. "Advanced regression and detection methods for remote sensing data analysis". Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2765/2/Castelletti-thesis.pdf.

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Nowadays the analysis of remote sensing data for environmental monitoring is fundamental to understand the local and global Earth dynamics. In this context, the main goal of this thesis is to present novel signal processing methods for the estimation of biophysical parameters and for the analysis icy terrain with active sensors. The thesis presents three main contributions. In the context of biophysical parameters estimation we focus on regression methods. According to the analysis of the literature, most of the regression techniques require a relevant number of reference samples to model a robust regression function. However, in real-word applications the ground truth observations are limited as their collection leads to high operational cost. Moreover, the availability of biased samples may result in low estimation accuracy. To address these issues, in this thesis we propose two novel contributions. The first contribution is a method for the estimation of biophysical parameters that integrates theoretical models with empirical observations associated to a small number of in-situ reference samples. The proposed method computes and correct deviations between estimates obtained through the inversion of theoretical models and empirical observations. The second contribution is a semisupervised learning (SSL) method for regression defined in the context of the ε-insensitive SVR. The proposed SSL method aims to mitigate the problems of small-sized biased training sets by injecting priors information in the initial learning of the SVR function, and jointly exploiting labeled and unlabeled samples in the learning phase of the SVR. The third contribution of this dissertation addresses the clutter detection problem in radar sounder (RS) data. The capability to detect clutter is fundamental for the interpretation of subsurface features in the radargram. In the state of the art, techniques that require accurate information on the surface topography or approaches that exploit complex multi-channel radar sounder systems have been presented. In this thesis, we propose a novel method for clutter detection that is independent from ancillary information and limits the hardware complexity of the radar system. The method relies on the interferometric analysis of two-channel RS data and discriminates the clutter and subsurface echoes by modeling the theoretical phase difference between the cross-track antennas of the RS. This allows the comparison of the phase difference distributions of real and simulated data. Qualitative and quantitative experimental results obtained on real airborne SAR and RS data confirm the effectiveness of the proposed methods.

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McLean, Andrew Lister. "Applications of maximum entropy data analysis". Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319161.

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Ortega-García, José Antonio. "Forest stand delineation through remote sensing and Object-Based Image Analysis". Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-28005.

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Forest stand delineation is an essential task of forest management planning which can be time consuming and exposed to subjectivity. The increasing availability of LiDAR data and multispectral imagery offers an opportunity to improve stand delineation by means of remotely-sensed data. Under these premises, ASTER imagery and low-density LiDAR data have been used to automatically delineate forest stands in several forests of Navarra (Spain) through Object-Based Image Analysis (OBIA). Canopy cover, mean height and the canopy model have been extracted from LiDAR data and, along with VNIR ASTER bands, introduced in OBIA for forest segmentation. The outcome of segmentation has been contrasted, on the one hand, assessing segments’ inner heterogeneity. On the other, OBIA’s segments and existing stand delineations have been compared with a new method of geometrical fitting which has been ad hoc designed for this study. Results suggest that low-density LiDAR and multispectral data, along with OBIA, are a powerful tool for stand delineation. Multispectral images have a limited predicting utility for species differentiation and, in practical terms, they help to discriminate between broad-leaved, conifer and mixed stands. The performance of ASTER data, though, could be improved with higher spatial resolution VNIR imagery, specifically sub-metric VNIR orthophotos. LiDAR data, in contrast, offers a great potential for forest structure depiction. This perspective is connected with the increasingly higher resolution datasets which are to be provided by public institutions and the rapid development of drone technology. Complexity of OBIA may limit the use of this technique for small consulting firms but it is an advisable instrument for companies and institutions involved in major forestry projects.
No

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Ducey, Craig David. "Hierarchical Image Analysis and Characterization of Scaling Effects in Remote Sensing". PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/399.

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The effects of scale influence all aspects of spatial analysis and should be expressly considered early in research planning. Remotely sensed images provide unique landscape perspectives and possess several features amenable to dealing with scale. In particular, images can be segmented into image objects representative of landscape features and structured as nested hierarchies for evaluating landscape patterns across a range of scales. The objectives of this research are to evaluate methods for: 1) characterizing candidate image objects to inform the selection of user-supplied segmentation parameters and 2) exploring the multi-scale structure of landscape patterns for defining and describing potentially important scales for conducting subsequent geospatial and ecological investigations. I followed a recursive strategy to develop an image hierarchy using a corrected version of the normalized difference vegetation index (NDVIc) derived from a Landsat ETM+ satellite image over a complex, forested landscape at Lava Cast Forest (LCF), Oregon. At each scale level, I calculated an objective function based on within-object variance and spatial autocorrelation to distinguish between alternative image objects created with the region-merging segmentation algorithm available in the Definiens Developer 7 software. Segmentation quality was considered highest for results exhibiting the lowest overall within-object variance and between-object spatial autocorrelation. I then applied geographical variance analysis to calculate the independent contribution and relative variability of each level in the hierarchy to evaluate the scene's spatial structure across scales. My results reveal overall trends in image object spatial variance consistent with scaling theory, but suggest judging image object quality without sampling the entire range of segmentation parameters is insufficient. Statistical limitations of the spatial autocorrelation coefficient at small sample sizes constrained the number of possible hierarchy levels within the image spatial extent, preventing identification of larger-scale landscape patterns. Geographical variance analysis results show patterns in vegetation conditions at LCF possess a multi-scaled structure. Three levels exhibiting high variance relative to the entire hierarchy coincide with abrupt transitions in the slopes of within-object variance and spatial autocorrelation trends, which I interpreted as scale thresholds potentially important for relating landscape patterns and processes. These methods provide an objective, object-oriented approach for addressing scale issues within heterogeneous landscapes using remote sensing.

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38

Cui, Yanwei. "Kernel-based learning on hierarchical image representations : applications to remote sensing data classification". Thesis, Lorient, 2017. http://www.theses.fr/2017LORIS448/document.

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La représentation d’image sous une forme hiérarchique a été largement utilisée dans un contexte de classification. Une telle représentation est capable de modéliser le contenu d’une image à travers une structure arborescente. Dans cette thèse, nous étudions les méthodes à noyaux qui permettent de prendre en entrée des données sous une forme structurée et de tenir compte des informations topologiques présentes dans chaque structure en concevant des noyaux structurés. Nous présentons un noyau structuré dédié aux structures telles que des arbres non ordonnés et des chemins (séquences de noeuds) équipés de caractéristiques numériques. Le noyau proposé, appelé Bag of Subpaths Kernel (BoSK), est formé en sommant les noyaux calculés sur les sous-chemins (un sac de tous les chemins et des noeuds simples) entre deux sacs. Le calcul direct de BoSK amène à une complexité quadratique par rapport à la taille de la structure (nombre de noeuds) et la quantité de données (taille de l’ensemble d’apprentissage). Nous proposons également une version rapide de notre algorithme, appelé Scalable BoSK (SBoSK), qui s’appuie sur la technique des Random Fourier Features pour projeter les données structurées dans un espace euclidien, où le produit scalaire du vecteur transformé est une approximation de BoSK. Cet algorithme bénéficie d’une complexité non plus linéaire mais quadratique par rapport aux tailles de la structure et de l’ensemble d’apprentissage, rendant ainsi le noyau adapté aux situations d’apprentissage à grande échelle. Grâce à (S)BoSK, nous sommes en mesure d’effectuer un apprentissage à partir d’informations présentes à plusieurs échelles dans les représentations hiérarchiques d’image. (S)BoSK fonctionne sur des chemins, permettant ainsi de tenir compte du contexte d’un pixel (feuille de la représentation hiérarchique) par l’intermédiaire de ses régions ancêtres à plusieurs échelles. Un tel modèle est utilisé dans la classification des images au niveau pixel. (S)BoSK fonctionne également sur les arbres, ce qui le rend capable de modéliser la composition d’un objet (racine de la représentation hiérarchique) et les relations topologiques entre ses sous-parties. Cette stratégie permet la classification des tuiles ou parties d’image. En poussant plus loin l’utilisation de (S)BoSK, nous introduisons une nouvelle approche de classification multi-source qui effectue la classification directement à partir d’une représentation hiérarchique construite à partir de deux images de la même scène prises à différentes résolutions, éventuellement selon différentes modalités. Les évaluations sur plusieurs jeux de données de télédétection disponibles dans la communauté illustrent la supériorité de (S)BoSK par rapport à l’état de l’art en termes de précision de classification, et les expériences menées sur une tâche de classification urbaine montrent la pertinence de l’approche de classification multi-source proposée
Hierarchical image representations have been widely used in the image classification context. Such representations are capable of modeling the content of an image through a tree structure. In this thesis, we investigate kernel-based strategies that make possible taking input data in a structured form and capturing the topological patterns inside each structure through designing structured kernels. We develop a structured kernel dedicated to unordered tree and path (sequence of nodes) structures equipped with numerical features, called Bag of Subpaths Kernel (BoSK). It is formed by summing up kernels computed on subpaths (a bag of all paths and single nodes) between two bags. The direct computation of BoSK yields a quadratic complexity w.r.t. both structure size (number of nodes) and amount of data (training size). We also propose a scalable version of BoSK (SBoSK for short), using Random Fourier Features technique to map the structured data in a randomized finite-dimensional Euclidean space, where inner product of the transformed feature vector approximates BoSK. It brings down the complexity from quadratic to linear w.r.t. structure size and amount of data, making the kernel compliant with the large-scale machine-learning context. Thanks to (S)BoSK, we are able to learn from cross-scale patterns in hierarchical image representations. (S)BoSK operates on paths, thus allowing modeling the context of a pixel (leaf of the hierarchical representation) through its ancestor regions at multiple scales. Such a model is used within pixel-based image classification. (S)BoSK also works on trees, making the kernel able to capture the composition of an object (top of the hierarchical representation) and the topological relationships among its subparts. This strategy allows tile/sub-image classification. Further relying on (S)BoSK, we introduce a novel multi-source classification approach that performs classification directly from a hierarchical image representation built from two images of the same scene taken at different resolutions, possibly with different modalities. Evaluations on several publicly available remote sensing datasets illustrate the superiority of (S)BoSK compared to state-of-the-art methods in terms of classification accuracy, and experiments on an urban classification task show the effectiveness of proposed multi-source classification approach

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Zhu, Shuxiang. "Big Data System to Support Natural Disaster Analysis". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592404690195316.

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Zhang, Hongqin. "Color in scientific visualization : perception and image-based data display /". Online version of thesis, 2008. http://hdl.handle.net/1850/5805.

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Mello, Marcio Pupin. "Spectral-temporal and Bayesian methods for agricultural remote sensing data analysis". Instituto Nacional de Pesquisas Espaciais (INPE), 2013. http://urlib.net/sid.inpe.br/mtc-m19/2013/09.17.18.58.

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Informações agrícolas confiáveis tem se tornado cada vez mais importantes para os tomadores de decisões. Especialmente quando são obtidas em tempo hábil, essas informações são altamente relevantes para o planejamento estratégico do país. Apesar de o sensoriamento remoto mostrar-se promissor para aplicações em mapeamento agrícola, com potencial de melhorar as estatísticas agrícolas oficiais, esse potencial não tem sido amplamente explorado. Existem poucos exemplos bem sucedidos do uso operacional do sensoriamento remoto para mapeamento sistemático de culturas agrícolas e, para garantir resultados precisos, eles são fortemente baseados em interpretação visual de imagens. De fato, apesar dos substanciais avanços em análise de dados de sensoriamento remoto, novas técnicas para automatizar a análise de dados em sensoriamento remoto com aplicações agrícolas são desejáveis, especialmente no propósito de manter a consistência e a precisão dos resultados. Neste contexto, existe uma demanda crescente pelo desenvolvimento e implementação de métodos automatizados de análise de dados de sensoriamento remoto com aplicações em agricultura. Assim, o principal objetivo desta tese é propor o desenvolvimento e a implementação de métodos para automatizar a análise de dados de sensoriamento remoto em aplicações agrícolas, com foco na consistência e precisão dos resultados. Este documento foi escrito como uma coleção de dois artigos, cada um com foco nos seguintes pontos: (i) análise multitemporal, multiespectral e multisensor, permitindo a descrição das variações espectrais de alvos agrícolas ao longo do tempo; e (ii) inteligência artificial na modelagem de fenômenos usando dados de sensoriamento remoto e informações complementares de maneira integrada. Dois estudos de caso referentes ao mapeamento da colheita da cana em São Paulo e ao mapeamento da soja no Mato Grosso foram usados para testar as metodologias batizadas de STARS e BayNeRD, respectivamente. Os resultados dos testes confirmaram que ambos os métodos propostos foram capazes de automatizar processos de análises de dados de sensoriamento remoto com aplicações agrícolas, com consistência e precisão.
Reliable agricultural statistics has become increasingly important to decision makers. Especially when timely obtained, agricultural information is highly relevant to the strategic planning of the country. Although remote sensing shows to be of great potential for agricultural mapping applications, with the benefit of further improving official agricultural statistics, its potential has not been fully explored. There are very few successful examples of operational remote sensing application for systematic mapping of agricultural crops, and they are strongly supported by visual image interpretation to allow accurate results. Indeed, despite the substantial advances in remote sensing data analysis, techniques to automate remote sensing data analysis focusing on agricultural mapping applications are highly valuable but have to maintain consistency and accuracy. In this context, there continues to be a demand for development and implementation of computer aided methods to automate the processes of analyzing remote sensing datasets for agriculture applications. Thus, the main objective of this thesis is to propose implementation of computer aided methodologies to automate, maintaining consistency and accuracy, processes of remote sensing data analyses focused on agricultural thematic mapping applications. This thesis was written as a collection of two papers related to a core theme, each addressing the following main points: (i) multitemporal, multispectral and multisensor image analysis that allow the description of spectral changes of agricultural targets over time; and (ii) artificial intelligence in modeling phenomena using remote sensing and ancillary data. Study cases of sugarcane harvest in São Paulo and soybean mapping in Mato Grosso were used to test the proposed methods named STARS and BayNeRD, respectively. The two methods developed and tested confirm that remotely sensed (and ancillary) data analysis can be automated with computer aided methods to model a range of cropland phenomena for agriculture applications, maintaining consistency and accuracy.

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Brooks, Evan B. "Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data". Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23276.

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Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation. There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate. Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements. In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001. Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels. Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels. We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data. The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm. EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery. We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values. These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential. These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters. Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data. They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects.
Ph. D.

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Laben, Craig A. "A comparison of methods for forming multitemporal composites from NOAA advanced very high resolution radiometer data /". Online version of thesis, 1993. http://hdl.handle.net/1850/12137.

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Welle, Paul. "Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis". Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1012.

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Policy analyses of agricultural and environmental systems are often limited due to data constraints. Measurement campaigns can be costly, especially when the area of interest includes oceans, forests, agricultural regions or other dispersed spatial domains. Satellite based remote sensing offers a way to increase the spatial and temporal resolution of policy analysis concerning these systems. However, there are key limitations to the implementation of satellite data. Uncertainty in data derived from remote-sensing can be significant, and traditional methods of policy analysis for managing uncertainty on large datasets can be computationally expensive. Moreover, while satellite data can increasingly offer estimates of some parameters such as weather or crop use, other information regarding demographic or economic data is unlikely to be estimated using these techniques. Managing these challenges in practical policy analysis remains a challenge. In this dissertation, I conduct five case studies which rely heavily on data sourced from orbital sensors. First, I assess the magnitude of climate and anthropogenic stress on coral reef ecosystems. Second, I conduct an impact assessment of soil salinity on California agriculture. Third, I measure the propensity of growers to adapt their cropping practices to soil salinization in agriculture. Fourth, I analyze whether small-scale desalination units could be applied on farms in California in order mitigate the effects of drought and salinization as well as prevent agricultural drainage from entering vulnerable ecosystems. And fifth, I assess the feasibility of satellite-based remote sensing for salinity measurement at global scale. Through these case studies, I confront both the challenges and benefits associated with implementing satellite based-remote sensing for improved policy analysis.

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Rajadell, Rojas Olga. "Data selection and spectral-spatial characterisation for hyperspectral image segmentation. Applications to remote sensing". Doctoral thesis, Universitat Jaume I, 2013. http://hdl.handle.net/10803/669093.

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El análisis de imágenes ha impulsado muchos descubrimientos en la ciencia actual. Esta tesis se centra en el análisis de imágenes remotas para inspección aérea, exactamente en el problema de segmentación y clasificación de acuerdo al uso del suelo. Desde el nacimiento de los sensores hiperespectrales su uso ha sido vital para esta tarea ya que facilitan y mejoran sustancialmente el resultado. Sin embargo el uso de imágenes hiperespectrales entraña, entre otros, problemas de dimensionalidad y de interacción con los expertos. Proponemos mejoras que ayuden a paliar estos inconvenientes y hagan el problema mas eficiente.
Lately image analysis have aided many discoveries in research. This thesis focusses on the analysis of remote sensed images for aerial inspection. It tackles the problem of segmentation and classification according to land usage. In this field, the use of hyperspectral images has been the trend followed since the emergence of hyperspectral sensors. This type of images improves the performance of the task but raises some issues. Two of those issues are the dimensionality and the interaction with experts. We propose enhancements overcome them. Efficiency and economic reasons encouraged to start this work. The enhancements introduced in this work allow to tackle segmentation and classification of this type of images using less data, thus increasing the efficiency and enabling the design task specific sensors which are cheaper. Also, our enhacements allow to perform the same task with less expert collaboration which also decreases the costs and accelerates the process.

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Yang, Bo. "Assimilation of multi-scale thermal remote sensing data using spatio-temporal cokriging method". University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868463.

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Munechika, Curtis K. "Merging panchromatic and multispectral images for enhanced image analysis /". Online version of thesis, 1990. http://hdl.handle.net/1850/11366.

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Rodriguez-Guerra, Edna Patricia. "Faulting evidence of isostatic uplift in the Rincon Mountains metamorphic core complex: An image processing analysis". Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284275.

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This study focuses on the applications of remote sensing techniques and digital analysis to characterizing of tectonic features of the Rincon Mountains metamorphic core complex. Data included Landsat Thematic Mapper (TM) images, digital elevation models (DEM), and digital orthophoto quadrangle quads (DOQQ). The main findings in this study are two nearly orthogonal systems of structures that have never been reported in the Rincon Mountains. The first system, a penetrative faulting system of the footwall rocks, trends N10-30°W. Similar structures identified in other metamorphic core complexes. The second system trends N60-70°E, and has only been alluded indirectly in the literature of metamorphic core complexes. The structures pervade mylonites in Tanque Verde Mountain, Mica Mountain, and the Rincon Peak area. As measured on the imagery, spacing between the N10-30°W lineaments ranges from ∼0.5 to 2 km, and from 0.25 to 1 km for the N60-70°E system. Field inspection reveals that the N10-30°W trending system, are high-angle normal faults dipping mainly to the west. One of the main faults, named here the Cabeza de Vaca fault, has a polished, planar, striated and grooved surface with slickenlines indicating pure normal dip-slip movement (N10°W, 83°SW; slickensides rake 85°SW). The Cabeza de Vaca fault is the eastern boundary of a 2 km-wide graben, with displacement as great as 400 meters. The N10-30°W faults are syn- to post-mylonitic, high-angle normal faults that formed during isostatic uplift of the Rincon core complex during mid-Tertiary time. This interpretation is based on previous works, which report similar fault patterns in other metamorphic core complexes. Faults trending N20-30°W, shape the east flank of Mica Mountain. These faults, on the back dipping mylonitic zone, dip east and may represent late-stage antithetic shear zones. The Cabeza de Vaca fault and the back dipping antithetic faults accommodate as much as 65% of the extension due to doming of the core complex. The N60-70°E structures, not verified as a fault system, are a joint system pervading the footwall rocks of the metamorphic core complex. This system is less systematic. Spacing varies from 0.25 to 1 km. Both systems control the drainage of the mountains.

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Thoué, Frédéric. "Quantification par imagerie tridimensionnelle de l'extension continentale et des déplacements associés : exemples au Kenya et au Yémen". Grenoble 1, 1993. http://www.theses.fr/1993GRE10200.

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La description de la geometrie et de la distribution des structures fragiles presentes dans les rifts contribuent a la comprehension des mecanismes de la deformation extensive a l'echelle de la lithosphere. Une analyse quantitative est envisagee sur une portion du rift est-africain (gregory rift au kenya) et sur la marge est de la mer rouge (yemen), a partir de donnees structurales, d'images satellitaires et de donnees topographiques. Les etudes de terrain permettent de preciser la geometrie et la chronologie des deformations pour chaque objet etudie (kenya ou yemen). Le traitement puis l'interpretation des images numeriques spot ou landsat permet (i) de completer les etudes structurales de terrain et de les integrer a petite echelle, (ii) de quantifier la distribution en 2d des failles sur le gregory rift. Ces etudes en 2d s'averent insuffisantes pour la quantification de l'extension. L'utilisation de modeles numeriques de terrain obtenus par auto-correlation de couples stereoscopiques spot conduit a la reconstitution de l'etat initial, ante-deformation, des systemes de blocs bascules qui se developpent au yemen. La restauration de l'etat initial des blocs bascules comporte une etape de basculement ou de depliage selon le modele realise, et une etape d'ajustement manuel des blocs entre eux. L'ajustement est realise en tenant compte de la forme des blocs ou des trajectoires de deformation donnees par le depliage des structures. La comparaison de l'etat initial restaure et la connaissance de l'etat final deforme permet une approche cinematique de l'extension, en terme de deplacements finis. La quantite d'extension est determinee. Les champs de deplacements 2d sont traces pour chaque secteur etudie. Les deplacements 3d sont calcules et projetes sur les plans de failles separant les blocs bascules, afin de calculer le tenseur de contraintes associe. L'interpretation des resultats obtenus fournit (i) les limites de validite de la methode de quantification, (ii) un modele de decoupage en blocs crustaux du sud de la plaque arabique, (iii) la geometrie des phases precoces lors de l'ouverture du golfe d'aden

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Al-Rousan, Naief Mahmoud. "System calibration, geometric accuracy testing and validation of DEM and orthoimage data extracted from spot stereo-pairs using commercially available image processing systems". Thesis, University of Glasgow, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264262.

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