Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

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In this thesis we develop algorithms for the processing of textures and images using a ladder-based biorthogonal directional filter bank (DFB). This work is based on the DFB originally proposed by Bamberger and Smith. First we present a novel implementation of this filter bank using ladder structures. This new DFB provides non-trivial FIR perfect reconstruction systems which are computationally very efficient. Furthermore we address the lack of shift-invariance in the DFB by presenting a novel undecimated DFB that preserves the computational simplicity of its maximally decimated counterpart. Finally, we study the use of the DFB in combination with pyramidal structures to form polar-separable image decompositions. Using the proposed filter banks we develop and evaluate algorithms for texture classification, segmentation and synthesis. We perform a comparative study with other image representations and find that the DFB provides some of the best results reported on the data sets used. Using the proposed directional pyramids we adapt wavelet thresholding algorithms. We find that our decompositions provide better edge and contour preservation than the best results reported using the undecimated discrete wavelet transform. Finally, we apply the developed algorithms to the analysis and processing of synthetic aperture radar (SAR) imagery. SAR image analysis is impaired by the presence of speckle noise. Our first objective will be to study the removal of speckle to enhance the visual quality of the image. Additionally, we implement land cover segmentation and classification algorithms taking advantage of the textural characteristics of SAR images. Finally, we propose a model-based SAR image compression algorithm in which the speckle component is separated from the structural features of a scene. The speckle component is captured with a texture model and the scene component is coded with a wavelet coder at very low bit rates. The resulting decompressed images have a better perceptual quality than SAR images compressed without removing speckle.

In the scope of this thesis, simulation-based analyses and comparative evaluation of Synthetic Aperture Radar (SAR) image formation techniques, namely Time Domain Correlation, Range Stacking, Range Doppler and Chirp Scaling algorithms, are presented. For this purpose, first, the fundamental concepts of SAR such as SAR geometry, resolution and signal properties are explained. A broadside SAR simulator that provides artificial raw data as an input to the algorithms is designed and implemented. Then, the mathematical background of the imaging algorithms discussed in the thesis is provided. Implementations of these algorithms and simulations are carried out using MATLAB&reg / . Finally, simulation results are presented and discussed to show the advantages and disadvantages of the algorithms.

Synthetic Aperture Radar (SAR) is an emerging technique in remote sensing. The technology is capable of producing high-resolution images of the earth surface in all-weather conditions. Thesis work describes the present available methods for positioning and imaging targets using M-sequence UWB (Ultra-Wideband) radar signals with moving antennas and SAR algorithm to retrieve position and image of the target. M-sequence UWB radar technology used as signal source for transmission and receiving echoes of target. Pseudo random binary sequence is used as a transmitted signal. These radars have an ability to penetrate signal through natural and unnatural objects. It offers low cost and quality security system. Among a number of techniques of image retrieval in Synthetic Aperture Radar, study of Global back projection (GBP) algorithm is presented. As a time domain algorithm, GBP possesses inherent advantages over frequency domain algorithm like ability to handle long integration angle, wider bandwidth and unlimited aperture size. GBP breaks the full synthesis aperture into numbers of sub-apertures. These sub-apertures are treated pixel by pixel. Each sub-aperture is converted to a Cartesian image grid to form an image.  During this conversion the signal is treated with linear interpolation methods in order to achieve the best quality of the images. The objective of this thesis is the imaging of target using M-sequence UWB radar and processing SAR raw data using Global back projection algorithm.

Cette thèse se situe dans le cadre de l'interprétation d'images satellite à haute résolution, et concerne plus spécifiquement la mise à jour de bases de données cartographiques grâce à des images optique et radar à haute résolution. Cette étude présente une chaîne de traitement générique pour la création ou la mise à jour de bases de données représentant les routes ou les bâtiments en milieu urbain. En fonction des données disponibles, différents scénarios sont envisagés. Le traitement est effectué en deux étapes. D'abord nous cherchons les objets qui doivent être retirés de la base de données. La seconde étape consiste à rechercher dans les images de nouveaux objets à ajouter dans la base de données. Pour réaliser ces deux étapes, des descripteurs sont construits dans le but de caractériser les objets d'intérêt dans les images d'entrée. L'inclusion ou élimination des objets dans la base de données est basée sur un score obtenu après fusion des descripteurs dans le cadre de la théorie de Dempster-Shafer. Les résultats présentés dans cette thèse illustrent l'intérêt d'une fusion multi-capteurs. De plus l'intégration aisée de nouveaux descripteurs permet à la chaîne d'être améliorable et adaptable à d'autres objets. / This work takes place in the framework of high resolution remote sensing image analysis. It focuses on the issue of cartographic database creation or updating with optical and SAR images. The goal of this work is to build a generic processing chain to update or create a cartographic database representing roads and buildings in built-up areas. According to available data, various scenarios are foreseen. The proposed processing chain is composed of two steps. First, if a database is available, the presence of each database object is checked in the images. The second step consist of looking for new objects that should be included in the database. To determine if an object should be present in the updated database, relevant features are extracted from images in the neighborhood of the considered object. Those features are based on caracteristics of roads and buildings in SAR and optical images. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of the Dempster-Shafer evidence theory. Results highlight the interest of multi sensor fusion. Moreover the chosen framework allows the easy integration of new features in the processing chain.

Cette thèse comprend deux axes de recherche. D´abord, un nouveau cadre méthodologique pour évaluer la conformité des données RSO (Radar à Synthèse d’Ouverture) multivariées à haute résolution spatiale est proposé en termes de statistique asymptotique par rapport au modèle produit. Plus précisément, la symétrie sphérique est étudiée en appliquant un test d'hypothèses sur la structure de la matrice de quadri-covariance. Deux jeux de données, simulées et réelles, sont prises en considération pour étudier la performance du test obtenu par l’analyse qualitative et quantitative des résultats. La conclusion la plus importante, en ce qui concerne la méthodologie employée dans l'analyse des données RSO multivariées, est que, selon les différents cas d’usages, une partie considérable de données hétérogènes peut ne pas s’ajuster asymptotiquement au modèle produit. Par conséquent, les algorithmes de classification et/ou détection conventionnels développés sur la base de celui-ci deviennent sub-optimaux. Cette observation met en évidence la nécessité de développer de modèles plus sophistiqués comme l'Analyse en Composantes Indépendantes, ce qui conduit à la deuxième partie de cette thèse qui consiste en l’étude du biais d’estimation des paramètres TSVM (Target Scattering Vector Model) lorsque l’ACP est utilisée. Enfin, les performances de l'algorithme sont également évaluées sous l'hypothèse du bruit gaussien corrélé spatialement. L’évaluation théorique de l'ACI comme un outil de type ICTD (In Coherent Target Decomposition) polarimétrique permet une analyse plus efficace de l’apport d’information fourni. A ce but, deux espaces de représentation sont utilisé, notamment H /alpha et TSVM / This thesis comprises two research axes. First, a new methodological framework to assess the conformity of multivariate high-resolution Synthetic Aperture Radar (SAR) data with respect to the Spherically Invariant Random Vector model in terms of asymptotic statistics is proposed. More precisely, spherical symmetry is investigated by applying statistical hypotheses testing on the structure of the quadricovariance matrix. Both simulated and real data are taken into consideration to investigate the performance of the derived test by a detailed qualitative and quantitative analysis. The most important conclusion drawn, regarding the methodology employed in analysing SAR data, is that, depending on the scenario under study, a considerable portion of high heterogeneous data may not fit the aforementioned model. Therefore, traditional detection and classification algorithms developed based on the latter become sub-optimal when applied in such kind of regions. This assertion highlights for the need of the development of model independent algorithms, like the Independent Component Analysis, what leads to the second part of the thesis. A Monte Carlo approach is performed in order to investigate the bias in estimating the Touzi's Target Scattering Vector Model (TSVM) parameters when ICA is employed using a sliding window approach under different scenarios. Finally, the performance of the algorithm is also evaluated under Gaussian clutter assumption and when spatial correlation is introduced in the model. These theoretical assessment of ICA based ICTD enables a more efficient analysis of the potential new information provided by the ICA based ICTD. Both Touzi TSVM as well as Cloude and Pottier H/alpha feature space are then taken into consideration for that purpose. The combined use of ICA and Touzi TSVM is straightforward, indicating new, but not groundbreaking information, when compared to the Eigenvector approach. Nevertheless, the analysis of the combined use of ICA and Cloude and Pottier H/alpha feature space revealed a potential aspect of the Independent Component Analysis based ICTD, which can not be matched by the Eigenvector approach. ICA does not introduce any unfeasible region in the H/alpha plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Esta tese introduz uma nova metodologia para determinar a direÃÃo do vento sobre a superfÃcie dos oceanos utilizando tÃcnicas de processamento das imagens de Radar de Abertura SintÃtica (SAR, do inglÃs Synthetic Aperture Radar). A literatura relacionada demonstra um crescente interesse no processamento dessas imagens para detecÃÃo de alvos, classificaÃÃo de regiÃes, extraÃÃo de campos de ventos, monitoramento de derrames de Ãleo, aplicaÃÃes geofÃsicas e meteorolÃgicas. A extraÃÃo de campos de ventos em imagens SAR à uma tarefa desafiadora devido à contaminaÃÃo das mesmas por um ruÃdo oriundo do sistema de aquisiÃÃo, denominado speckle, que dificulta tarefas de processamento e interpretaÃÃo das mesmas. Portanto, esta tese propÃe metodologias de extraÃÃo da direÃÃo do vento por transformada de Fourier, transformadas wavelets e mÃtodos baseados em textura. As transformadas wavelets utilizadas para esta tarefa sÃo Gabor, ChapÃu Mexicano e o algoritmo à trous. Com relaÃÃo à anÃlise de textura utilizada, esta se baseia na informaÃÃo espacial da matriz de co-ocorrÃncia dos nÃveis de cinza para estimar a direÃÃo de padrÃes lineares em imagens contaminadas com speckle. Os experimentos foram realizados em imagens de textura sintÃticas, imagens do Ãlbum de Brodatz e imagens SAR sintÃticas e reais. Foi observado que os mÃtodos propostos foram capazes de estimar direÃÃes de padrÃes lineares e extrair campos de streaks de vento visÃveis em imagens SAR reais. As principais contribuiÃÃes desta tese sÃo: o mÃtodo proposto para estimaÃÃo de direÃÃo de ventos na superfÃcie do oceano e a extensÃo de tÃcnica jà existente na literatura, possibilitando assim a estimaÃÃo da velocidade dos ventos na faixa de 4 a 10 m/s. Os melhores resultados obtidos nesta tese foram alcanÃados utilizando o mÃtodo proposto que combina transformada wavelet e anÃlise de textura. / This thesis introduces a new methodology to determine the wind direction over the ocean surface using image processing techniques on SAR (Synthetic Aperture Radar) images. Related literature demonstrates a growing interest in processing these images for target detection, region classification, wind field extraction, oil spill monitoring, geophysical and meteorological applications. Wind field extraction in SAR images is a challenging task due to contamination acquisition system by speckle noise, which makes difficult processing and interpretation tasks. Thus, this thesis proposes methods for wind direction estimation by applying image transforms, such as Fourier and wavelets and furthermore texture-based methods. The wavelet transforms used for this task are Gabor, Mexican Hat and the à trous algorithm. Concerning the texture approach, it is based on the co-occurrence matrix to estimate direction of linear patterns in speckled images. The experiments were performed on synthetic texture, Brodatz album, synthetic and real SAR images. It was observed that the proposed methods were able to estimate directions of linear patterns and extract wind fields from visible wind-induced streaks on SAR images. The main contributions of this thesis are: to propose methods for wind direction estimation on the ocean surface and to extend existing techniques in the literature in order to provide wind vector estimation in the range of 4 to 10 m/s. The best results of this tese were achieved with the proposed method that combines wavelet transform and texture analysis.

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