En raison de l'importance du riz dans l'alimentation mondiale et du rôle des rizières dans les émissions de méthane, un suivi à grande échelle et en temps quasi-réel des surfaces cultivées en riz semble particulièrement utile. L'objectif de cette thèse est de développer des méthodes permettant une utilisation effective des données de télédétection des satellites présents et futurs pour le suivi des rizières. L'imagerie radar est privilégiée car elle permet des acquisitions sous toutes les conditions météorologiques, contrairement à l'imagerie optique. Deux méthodes sont retenues qui font intervenir un rapport d'intensité de deux images SAR en bande C : le rapport de polarisation HH/VV ou le changement temporel en co-polarisation HHdate2/HHdate1. Dans un premier temps, une étude statistique des rapports d'intensité de rétrodiffusion est effectuée, qui conduit au développement d'un modèle d'erreur permettant d'estimer la performance des méthodes de classification. Ce modèle d'erreur est également utilisé pour évaluer l'impact des paramètres des systèmes SAR (Synthetic Aperture Radar) sur la performance de la classification. Il s'agit des paramètres concernant l'étalonnage, l'ambiguïté, la fréquence de revisite. Dans un second temps, les méthodes de classification ainsi développées sont appliquées à deux jeux de données de l'instrument ASAR du satellite ENVISAT sur le delta du Mékong au Vietnam, pour faire la cartographie des rizières à deux échelles différentes. La première méthode repose sur l'utilisation du rapport HH/VV à partir de données du mode Alternating Polarization d'ASAR, qui permet de produire une carte de rizières couvrant une province du delta. La seconde méthode tire parti du changement temporel de HH sur des images du mode Wide-Swath d'ASAR, et est utilisée pour cartographier les rizières de l'ensemble du delta. Les deux méthodes sont validées avec succès en utilisant les surfaces cultivées données par les statistiques nationales.

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.

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.

No description available.

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.

For synthetic aperture imaging, position and orientation deviation is of great concern. Unknown motions of a Synthetic Aperture Sonar (SAS) can blur the reconstructed images and degrade image quality considerably. Considering the high sensitivity of synthetic aperture imaging technique to sonar deviation, this research aims at providing a thorough navigation solution for a free-towed synthetic aperture sonar (SAS) comprising aspects from the design and construction of the navigation card through to data postprocessing to produce position, velocity, and attitude information of the sonar. The sensor configuration of the designed navigation card is low-cost Micro-Electro-Mechanical-Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors including three angular rate gyroscopes, three dual-axial accelerometers, and a triaxial magnetic hybrid. These MARG sensors are mounted orthogonally on a standard 180mm Eurocard PCB to monitor the motions of the sonar in six degrees of freedom. Sensor calibration algorithms are presented for each individual sensor according to its characteristics to precisely determine sensor parameters. The nonlinear least square method and two-step estimator are particularly used for the calibration of accelerometers and magnetometers. A quaternion-based extended Kalman filter is developed based on a total state space model to fuse the calibrated navigation data. In the model, the frame transformations are described using quaternions instead of other attitude representations. The simulations and experimental results are demonstrated in this thesis to verify the capability of the sensor fusion strategy.