Los Radares de Apertura Sintética, o sistemas SAR, representan el mejorejemplo de sistemas activos de teledetección por microondas. Debido a su naturaleza coherente, un sistema SAR es capaz de adquirir información dedispersión electromagnética con una alta resolución espacial, pero por otro lado, esta naturaleza coherente provoca también la aparición de speckle.A pesar de que el speckle es una medida electromagnética, sólo puede ser analizada como una componente de ruido debido a la complejidad asociadacon el proceso de dispersión electromagnética.Para eliminar los efectos del ruido speckle adecuadamente, es necesario un modelo de ruido, capaz de identificar las fuentes de ruido y como éstasdegradan la información útil. Mientras que este modelo existe para sistemasSAR unidimensionales, conocido como modelo de ruido speckle multiplicativo,éste no existe en el caso de sistemas SAR multidimensionales.El trabajo presentado en esta tesis presenta la definición y completa validación de nuevos modelos de ruido speckle para sistemas SAR multidimensionales,junto con su aplicación para la reducción de ruido speckle y la extracción de información.En esta tesis, los datos SAR multidimensionales, se consideran bajo una formulación basada en la matriz de covarianza, ya que permite el análisisde datos sobre la base del producto complejo Hermítico de pares de imágenesSAR. Debido a que el mantenimiento de la resolución especial es un aspectoimportante del procesado de imágenes SAR, la reducción de ruido speckleestá basada, en este trabajo, en la teoría de análisis wavelet.

Scene classification is performed on countless remote sensing images in support of operational activities. Automating this process is preferable since manual pixel-level classification is not feasible for large scenes. However, developing such an algorithmic solution is a challenging task due to both scene complexities and sensor limitations. The objective is to develop efficient and accurate unsupervised methods for classification (i.e., assigning each pixel to an appropriate generic class) and for labeling (i.e., properly assigning true labels to each class). Unique from traditional approaches, the proposed bootstrap approach achieves classification and labeling without training data. Here, the full image is partitioned into subimages and the true classes found in each subimage are provided by the user. After these steps, the rest of the process is automatic. Each subimage is individually classified into regions and then using the joint information from all subimages and regions the optimal configuration of labels is found based on an objective function based on a Markov random field (MRF) model. The bootstrap approach has been successfully demonstrated with SAR sea-ice and lake ice images which represent challenging scenes used operationally for ship navigation, climate study, and ice fraction estimation. Accuracy assessment is based on evaluation conducted by third party experts. The bootstrap method is also demonstrated using synthetic and natural images. The impact of this technique is a repeatable and accurate methodology that generates classified maps faster than the standard methodology.

This thesis presents a new technique for performing image analysis, synthesis, and modification using a generalized Gaussian model. The joint time-frequency characteristics of a generalized Gaussian are combined with the flexibility of the analysis-by-synthesis (ABS) decomposition technique to form the basis of the model. The good localization properties of the Gaussian make it an appealing basis function for image analysis, while the ABS process provides a more flexible representation with enhanced functionality. ABS was first explored in conjunction with sinusoidal modeling of speech and audio signals [George87]. A 2D extension of the ABS technique is developed here to perform the image decomposition. This model forms the basis for new approaches in image analysis and enhancement. The major contribution is made in the resolution enhancement of images generated using coherent imaging modalities such as Synthetic Aperture Radar (SAR) and ultrasound. The ABS generalized Gaussian model is used to decouple natural image features from the speckle and facilitate independent control over feature characteristics and speckle granularity. This has the beneficial effect of increasing the perceived resolution and reducing the obtrusiveness of the speckle while preserving the edges and the definition of the image features. A consequence of its inherent flexibility, the model does not preclude image processing applications for non-coherent image data. This is illustrated by its application as a feature extraction tool for a FLIR imagery complexity measure.

In Inverse Synthetic Aperture Radar (ISAR) systems the motion of the target can be classified in two main categories: Translational Motion and Rotational Motion. A small degree of rotational motion is required in order to generate the synthetic aperture of the ISAR systems. On the other hand, the remaining part of the target&rsquo / s motion, that is any degree of translational motion and the large degree of rotational motion, degrades ISAR image quality. Motion compensation techniques focus on eliminating the effect of the targets&rsquo / motion on the ISAR images. In this thesis, ISAR image generation is discussed using both Conventional Fourier Based and Time-Frequency Based techniques. Standard translational motion compensation steps, Range and Doppler Tracking, are examined. Cross-correlation method and Dominant Scatterer Algorithm are employed for Range and Doppler tracking purposes, respectively. Finally, Time-Frequency based motion compensation is studied and compared with the conventional techniques. All of the motion compensation steps are examined using the simulated data. Stepped frequency waveforms are used in order to generate the required data of the simulations. Not only successful results, but also worst case examinations and lack of algorithms are also discussed with the examples.

Inverse Synthetic Aperture Radar coherently processes the return signal from the target in order to construct the image of the target. The conventional methodology used for obtaining the image is the Fourier transform which is not capable of suppressing the Doppler change in the return signal. As a result, Range-Doppler image is degraded. A proper time-frequency transform suppresses the degradation due to time varying Doppler shift. In this thesis, high resolution joint-time frequency transformations that can be used in place of the conventional method are evaluated. Wigner-Ville Distribution, Adaptive Gabor Representation with Coarse-to-Fine search algorithm, and Time-Frequency Distribution Series are examined for the target imaging system. The techniques applied to sample signals compared with each other. The computational and memorial complexity of the methods are evaluated and compared to each other and possible improvements are discussed. The application of these techniques in the target imaging system is also performed and resulting images compared to each other.

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Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use.

Scene classification is performed on countless remote sensing images in support of operational activities. Automating this process is preferable since manual pixel-level classification is not feasible for large scenes. However, developing such an algorithmic solution is a challenging task due to both scene complexities and sensor limitations. The objective is to develop efficient and accurate unsupervised methods for classification (i.e., assigning each pixel to an appropriate generic class) and for labeling (i.e., properly assigning true labels to each class). Unique from traditional approaches, the proposed bootstrap approach achieves classification and labeling without training data. Here, the full image is partitioned into subimages and the true classes found in each subimage are provided by the user. After these steps, the rest of the process is automatic. Each subimage is individually classified into regions and then using the joint information from all subimages and regions the optimal configuration of labels is found based on an objective function based on a Markov random field (MRF) model. The bootstrap approach has been successfully demonstrated with SAR sea-ice and lake ice images which represent challenging scenes used operationally for ship navigation, climate study, and ice fraction estimation. Accuracy assessment is based on evaluation conducted by third party experts. The bootstrap method is also demonstrated using synthetic and natural images. The impact of this technique is a repeatable and accurate methodology that generates classified maps faster than the standard methodology.

A proper time-frequency transform technique suppresses the blurring and smearing effect of the time-varying Doppler shift on the target image. The conventional target imaging method uses the Fourier transform for extracting the Doppler shift from the received radar pulse. Since the Doppler shift is timevarying for rotating targets, the constructed images will be degraded. In this thesis, the Doppler shift information required for the Range-Doppler image of the target is extracted by using high resolution time-frequency transform techniques. The Wigner-Ville Distribution and the Adaptive Gabor Representation with the Coarse-to-Fine and the Matching Pursuit Search Algorithms are examined techniques for the target imaging system. The modified Matching Pursuit Algorithm, the Matching Pursuit with Reduced Dictionary is proposed which decreases the signal processing time required by the Adaptive Gabor Representation. The Hybrid Matching Pursuit Search Algorithm is also introduced in this thesis work and the Coarse-to-Fine Algorithm and the Matching Pursuit Algorithm are combined for obtaining better representation quality of a signal in the time-frequency domain. The stated techniques are applied on to the sample signals and compared with each other. The application of these techniques in the target imaging system is also performed for the simulated aircrafts.

L'Indonésie, l’une de plus grands archipels, a été menacé avec la pollution provenant de la marée noire. Le gouvernement d’Indonésie en coopération avec le gouvernement Français a développé un système d'observation de l'océan par satellite afin de supporter de développement durable. Ce système est intégré dans les systèmes d'océanographie opérationnelle dans le cadre du projet de développement des infrastructures de l'océanographie spatiale (INDESO). Le contexte de cette thèse est dans le cadre du projet INDESO notamment dans applications d’INDESO pour suivre des déversements de pétrole dans les mers d’Indonésie. Dans ce contexte,cette thèse propose de nouvelles méthodologies et analyses. Cette thèse comportait deux contributions principales. La première contribution est sur la récupération des paramètres de dérive des déversements d'hydrocarbures à partir d'une analyse conjointe des observations SAR (Synthetic Aperture Radar) et des résultats d'un modèle de transport de déversement de pétrole. Dans cette première partie, nous estimons les paramètres de dérive de pétrole. On a exploité un modèle de transport de déversement de pétrole lagrangien,de sorte que la dérive simulée de déversement d'hydrocarbures modèles puisse correspondre à l'observation de satellite. Pour confirmer l'origine du déversement de pétrole détecté à une date donnée par une observation de SAR, nous avons effectué des simulations avec différentes dates de début de fuite, duré de fuite et différentes valeurs de pondération deux facteurs dominants i.e. vent et courant. Nous avons développé une nouvelle méthode pour l'assimilation de ces paramètres de fuite de pétrole à comparer avec d'une détection dérivée d'un déversement d'hydrocarbures. Nous avons appliqué la méthodologie proposée sur le plus grand accident en Indonésie, l'accident de Montara. La deuxième contribution est l'évaluation globale du risque de déversement d'hydrocarbures en Indonésie. Nous sommes concentrés sur la zone de gestion des pêches de l'Indonésie. Dans cette analyse, nous avons proposé une méthodologie qui considère le déversement de pétrole, qui a des sources différentes et leurs impacts à l'environnement, mais aussi sur les perspectives sociales et économiques. Pour l'évaluation de la vulnérabilité des zones marines protégées, nous avons également exploité le modèle de 2D lagrangien. L'accent mis sur les zones de gestion des pêches (FMA) afin de fournir une analyse synoptique sur l'ensemble du territoire maritime d’Indonésie. Chaque FMA présente les caractéristiques spécifiques des paramètres environnementaux etsocio-économiques. Nous avons évalué le risque de déversement d'hydrocarbures dans chaque zone de gestion sur la base de tous ces facteurs. Le résultat de cette étude peut être utilisé dans la planification d'une action pour réduire les impacts négatifs du déversement d'hydrocarbures. / Indonesia as the biggest archipelago has a major threat coming from oil spill. Due to the increasing concerns of environment protection for sustainable development, the government of Indonesia in cooperation with government of France developed an ocean observation system with one of its pilot applications is oil spills monitoring. This system is integrated in the operational oceanography systems within the project of Infrastructure Development of Space Oceanography (INDESO). The context of this thesis is in the frame of INDESO project particularly in the monitoring of oil spill in the Indonesian seas. Within the context above, this thesis propose new methodologies and analyses. This thesis involved two main contributions. The first contribution addressed the retrieval of oil spill drift parameters from a joint analysis of SAR observations of an oil spill and of outputs of a Lagrangian oil spill transport model. In this first part, we estimate oil spill drift parameters. The proposed framework exploited a Lagrangian oil spill transport model such that the simulated oil spill drift could match a SAR-based observation of an oil spill. In the considered 2D Lagrangian model there were two dominant factors, i.e. wind and surface current. To confirm the origin of the oil spill detected on a given date through a SAR observation, we performed simulations with various leakage starting dates, leakage durations, and different values of wind and current weighing coefficients. We developed a novel framework for the assimilation of these oil leakage parameters from a SAR-derived detection of an oil spill. We applied the proposed methodology on the most famous oil spill accident in Indonesia, the Montara case. The second contribution was the global assessment of oil spill risk inIndonesia. We focused on the 11 Indonesia Fisheries Management Area to support the sustainability development of marine and fisheries. In this analysis we proposed methodology that considered the oil spillfrom different source and their impacts not only to the environment, but also from social and economic perspectives. For the assessment of vulnerability of Marine Protected Areas to oil spill pollution, we also exploited the oil spill trajectory model. The focus was given to Fisheries Management Areas as a means to provide synoptic analysis over theentire Indonesian maritime territory. Using different information from many institutional reports, we collected and analyzed the potential source of oil spill in each FMA. Each FMA has specific characteristics in terms environmental and socioeconomic features. We assessed the oil spill risk in each FMA based on all these factors. The result of this study can be used in the mitigation planning to reduce the negative impacts of oil spill.