Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints

This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods

Extraction d'informations de changement à partir des séries temporelles d'images radar à synthèse d'ouverture

A large number of successfully launched and operated Synthetic Aperture Radar (SAR) satellites has regularly provided multitemporal SAR and polarimetric SAR (PolSAR) images with high and very high spatial resolution over immense areas of the Earth surface. SAR system is appropriate for monitoring tasks thanks to the advantage of operating in all-time and all-weather conditions. With multitemporal data, both spatial and temporal information can simultaneously be exploited to improve the results of researche works. Change detection of specific features within a certain time interval has to deal with a complex processing of SAR data and the so-called speckle which affects the backscattered signal as multiplicative noise.The aim of this thesis is to provide a methodology for simplifying the analysis of multitemporal SAR data. Such methodology can benefit from the advantages of repetitive SAR acquisitions and be able to process different kinds of SAR data (i.e. single, multipolarization SAR, etc.) for various applications. In this thesis, we first propose a general framework based on a spatio-temporal information matrix called emph{Change Detection Matrix} (CDM). This matrix contains temporal neighborhoods which are adaptive to changed and unchanged areas thanks to similarity cross tests. Then, the proposed method is used to perform three different tasks:1) multitemporal change detection with different kinds of changes, which allows the combination of multitemporal pair-wise change maps to improve the performance of change detection result;2) analysis of change dynamics in the observed area, which allows the investigation of temporal evolution of objects of interest;3) nonlocal temporal mean filtering of SAR/PolSAR image time series, which allows us to avoid smoothing change information in the time series during the filtering process.In order to illustrate the relevancy of the proposed method, the experimental works of the thesis is performed on four datasets over two test-sites: Chamonix Mont-Blanc, France and Merapi volcano, Indonesia, with different types of changes (i.e., seasonal evolution, glaciers, volcanic eruption, etc.). Observations of these test-sites are performed on four SAR images time series from single polarization to full polarization, from medium to high, very high spatial resolution: Sentinel-1, ALOS-PALSAR, RADARSAT-2 and TerraSAR-X time series.La réussite du lancement d'un grand nombre des satellites Radar à Synthèse d'Ouverture (RSO - SAR) de nouvelle génération a fourni régulièrement des images SAR et SAR polarimétrique (PolSAR) multitemporelles à haute et très haute résolution spatiale sur de larges régions de la surface de la Terre. Le système SAR est approprié pour des tâches de surveillance continue ou il offre l'avantage d'être indépendant de l'éclairement solaire et de la couverture nuageuse. Avec des données multitemporelles, l'information spatiale et temporelle peut être exploitée simultanément pour rendre plus concise, l'extraction d'information à partir des données. La détection de changement de structures spécifiques dans un certain intervalle de temps nécessite un traitement complexe des données SAR et la présence du chatoiement (speckle) qui affecte la rétrodiffusion comme un bruit multiplicatif. Le but de cette thèse est de fournir une méthodologie pour simplifier l'analyse des données multitemporelles SAR. Cette méthodologie doit bénéficier des avantages d'acquisitions SAR répétitives et être capable de traiter différents types de données SAR (images SAR mono-, multi- composantes, etc.) pour diverses applications. Au cours de cette thèse, nous proposons tout d'abord une méthode générale basée sur une matrice d'information spatio-temporelle appelée Matrice de détection de changement (CDM). Cette matrice contient des informations de changements obtenus à partir de tests croisés de similarité sur des voisinages adaptatifs. La méthode proposée est ensuite exploitée pour réaliser trois tâches différentes: 1) la détection de changement multitemporel avec différents types de changements, ce qui permet la combinaison des cartes de changement entre des paires d'images pour améliorer la performance de résultat de détection de changement; 2) l'analyse de la dynamicité de changement de la zone observée, ce qui permet l'étude de l'évolution temporelle des objets d'intérêt; 3) le filtrage nonlocal temporel des séries temporelles d'images SAR/PolSAR, ce qui permet d'éviter le lissage des informations de changement dans des séries pendant le processus de filtrage.Afin d'illustrer la pertinence de la méthode proposée, la partie expérimentale de la thèse est effectuée sur deux sites d'étude: Chamonix Mont-Blanc, France et le volcan Merapi, Indonésie, avec différents types de changements (i.e. évolution saisonnière, glaciers, éruption volcanique, etc.). Les observations de ces sites d'étude sont acquises sur quatre séries temporelles d'images SAR monocomposantes et multicomposantes de moyenne à haute et très haute résolution: des séries temporelles d'images Sentinel-1, ALOS-PALSAR, RADARSAT-2 et TerraSAR-X

Identificação de áreas inundáveis na porção sul de Roraima com auxílio de imagens de radar

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2018.As áreas úmidas são de extrema importância para o equilíbrio da vida na Terra, compreender o seu comportamento é crucial para a preservação destes ecossistemas. Muitas dessas áreas, como as inseridas em domínio amazônico, são de difícil acesso para um estudo em campo. As técnicas de Sensoriamento Remoto têm evoluído muito, aliadas aos avanços da inteligência artificial, contribuindo para o desenvolvimento de estudos em locais de difícil acesso, possibilitando bons resultados de acordo com o tipo de dado utilizado. O presente estudo utilizou a técnica de aprendizado de máquina Random Forest, e limiar de retroespalhamento em imagens SAR do satélite Sentinel 1, da Agência Espacial Europeia, para identificar áreas úmidas no sul do estado de Roraima ao longo do ano de 2017. O trabalho identificou o comportamento típico de áreas inundáveis nas margens dos rios e feições adjacentes ao principal rio do estado (rio Branco). O pico de cheia foi encontrado no mês de Julho, cobrindo mais de 72.000 hectares da área de estudo (11,9% da área total da cena), e o de seca no mês de Dezembro, cobrindo mais de 37.000 hectares (6,1% da área total da cena).Wetlands are of extreme importance for the balance of life on Earth, understanding their behavior is crucial to the preservation of these ecosystems. Many of these areas, such as those in the Amazonian domain, are difficult to access for field study. The techniques of Remote Sensing have evolved a lot, combined with the advances of artificial intelligence, contributing to the development of studies in places of difficult access, allowing good results according to the type of data used. The present study used the Random Forest machine learning technique and backscatter threshold in SAR images of the Sentinel 1 satellite of the European Space Agency to identify wetlands in the south of the state of Roraima throughout 2017. The work identified the typical behavior of flooded areas on the banks of the rivers and features adjacent to the main river of the state (Rio Branco). The flood peak was found in July, covering more than 72,000 hectares of the study area (11.9% of the total area of the scene), and the dry season in December, covering more than 37,000 hectares (6.1 % of the total area of the scene)

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Combination of optical and SAR remote sensing data for wetland mapping and monitoring

Wetlands provide many services to the environment and humans. They play a pivotal role in water quality, climate change, as well as carbon and hydrological cycles. Wetlands are environmental health indicators because of their contributions to plant and animal habitats. While a large portion of Newfoundland and Labrador (NL) is covered by wetlands, no significant efforts had been conducted to identify and monitor these valuable environments when I initiated this project. At that time, there were only two small areas in NL that had been classified using basic Remote Sensing (RS) methods with low accuracies. There was an immediate need to develop new methods for conserving and managing these vital resources using up-to-date maps of wetland distributions. In this thesis, object- and pixel-based classification methods were compared to show the high potential of the former method when medium or high spatial resolution imagery were used to classify wetlands. The maps produced using several classification algorithms were also compared to select the optimum classifier for future experiments. Moreover, a novel Multiple Classifier System (MCS), which combined several algorithms, was proposed to increase the classification accuracy of complex and similar land covers, such as wetlands. Landsat-8 images captured in different months were also investigated to select the time, for which wetlands had the highest separability using the Random Forest (RF) algorithm. Additionally, various spectral, polarimetric, texture, and ratio features extracted from multi-source optical and Synthetic Aperture Radar (SAR) data were assessed to select the most effective features for discriminating wetland classes. The methods developed during this dissertation were validated in five study areas to show their effectiveness. Finally, in collaboration with a team, a website (http://nlwetlands.ca/) and a software package were developed (named the Advanced Remote Sensing Lab (ARSeL)) to automatically preprocess optical/SAR data and classify wetlands using advanced algorithms. In summary, the outputs of this work are promising and can be incorporated into future studies related to wetlands. The province can also benefit from the results in many ways

Entwicklung einer übertragbaren, synergistischen Methode zur Kartierung von Biotoptypen anhand von hochauflösenden optischen und Radar-basierten Daten

Das übergeordnete Ziel der Arbeit war es zu evaluieren, in welchem Umfang die synergistische Verwendung von modernen Erdbeobachtungsdaten und -methoden zur Kartierung von Biotoptyp- und Landnutzungsinformationen beitragen kann. Anhand einer umfangreichen Literaturrecherche wurden die traditionellen Methoden der Biotoptypenkartierung und der Stand der Forschung im Bereich der Verwendung von Fernerkundungsinformationen für die Biotoptypenkartierung analysiert und Forschungsdefizite aufgezeigt, sowie Ansatzpunkte für eine Weiterentwicklung definiert. Hieraus ergaben sich die folgenden vier übergeordneten Forschungs- beziehungsweise Arbeitsschwerpunkte, welche im Verlauf der Arbeit noch weiter unterteilt wurden: 1. Die Analyse und Extraktion von potenziellen Informationen (Merkmalen) aus den vorliegenden Geoinformationen und die anschließende Reduktion der potenziellen Merkmale auf die relevanten Merkmale für die Kartierung der Biotoptyp- und Landnutzungsinformationen. 2. Die Entwicklung eines Klassifikationsansatzes für die Erfassung der Biotoptypen- und Landnutzungsinformationen anhand eines Entwicklungsdatensatzes. 3. Die Evaluation der Robustheit der Methode mittels Übertragung auf zwei weitere Datensätze. 4. Die Evaluation der Synergie der zugrundliegenden Geoinformationen. Es konnte gezeigt werden, dass das Ziel der Entwicklung einer übertragbaren, synergistischen Methode zur Kartierung von Biotoptypen anhand von hochauflösenden optischen und Radar-basierten Daten erreicht werden konnte. Die entstandenen Karten können als Hilfe für die Entscheidungsfindung im Bereich der Anforderungen der nationalen und internationalen Naturschutzrichtlinien dienen. Die gezeigten Ergebnisse im Bereich der Übertragbarkeit lassen darauf hoffen, dass die entwickelte Methode und die daraus entstehenden Ergebnisse auch in anderen Ökoregionen einsetzbar sind