Détection de bateaux dans les images de radar à ouverture synthétique

Le but principal de cette thèse est de développer des algorithmes efficaces et de concevoir un système pour la détection de bateaux dans les images Radar à Ouverture Synthetique (ROS.) Dans notre cas, la détection de bateaux implique en premier lieu la détection de cibles de points dans les images ROS. Ensuite, la détection d'un bateau proprement dit dépend des propriétés physiques du bateau lui-même, tel que sa taille, sa forme, sa structure, son orientation relative a la direction de regard du radar et les conditions générales de l'état de la mer. Notre stratégie est de détecter toutes les cibles de bateaux possibles dans les images de ROS, et ensuite de chercher autour de chaque candidat des évidences telle que les sillons. Les objectifs de notre recherche sont (1) d'améliorer 1'estimation des paramètres dans Ie modèle de distribution-K et de déterminer les conditions dans lesquelles un modèle alternatif (Ie Gamma, par exemple) devrait être utilise plutôt; (2) d'explorer Ie modèle PNN (Probabilistic Neural Network) comme une alternative aux modèles paramétriques actuellement utilises; (3) de concevoir un modèle de regroupement flou (FC : Fuzzy Clustering) capable de détecter les petites et grandes cibles de bateaux dans les images a un seul canal ou les images a multi-canaux; (4) de combiner la détection de sillons avec la détection de cibles de bateaux; (5) de concevoir un modèle de détection qui peut être utilisé aussi pour la détection des cibles de bateaux en zones costières.Abstract: The main purpose of this thesis is to develop efficient algorithms and design a system for ship detection from Synthetic Aperture Radar (SAR) imagery. Ship detection usually involves through detection of point targets on a radar clutter background.The detection of a ship depends on the physical properties of the ship itself, such as size, shape, and structure; its orientation relative to the radar look-direction; and the general condition of the sea state. Our strategy is to detect all possible ship targets in SAR images, and then search around each candidate for the wake as further evidence.The objectives of our research are (1) to improve estimation of the parameters in the K-distribution model and to determine the conditions in which an alternative model (Gamma, for example) should be used instead; (2) to explore a PNN (Probabilistic Neural Networks) model as an alternative to the commonly used parameteric models; (3) to design a FC (Fuzzy Clustering) model capable of detecting both small and large ship targets from single-channel images or multi-channel images; (4) to combine wake detection with ship target detection; (5) to design a detection model that can also be used to detect ship targets in coastal areas. We have developed algorithms for each of these objectives and integrated them into a system comprising six models.The system has been tested on a number of SAR images (SEASAT, ERS and RADARSAT-1, for example) and its performance has been assessed

A Dempster-Shafer Method for Multi-Sensor Fusion

The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi-sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster-Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur

Cartographie de paramètres forestiers par fusion évidentielle de données géospatiales multi-sources application aux peuplements forestiers en régénération et feuillus matures du Sud du Québec

Foresters are faced with difficulties to obtain sub-polygon information with the mapping methods available nowadays. The main objective of this work consisted in the development of new methods able to improve the map accuracy of regenerating forest stands and mature forest stands in the South of Québec, Canada. The Dempster-Shafer Theory (DST) and the Dezert-Smarandache Theory (DSmT) showed their ability to integrate multiple heterogenous data sources to go further than the classical classification procedures like the maximum likelihood or the spectral unmixing, in terms of map accuracy. Improvement on the ability to map regenerating stands, passed from 82.7% with the maximum likelihood method to 91.1% with the Free DSm model with a total transfer of the mass of the"Union" class to the"Intersection" class (+ 8.4%). For the mature stands, the improvement passed from 63.8% with the K nearest neighbour to 79.5% with the DST according to a classical belief structuration and the hybrid decision rule for which the conflict threshold was fixed at 10% (+ 15.7%). Our results with DST and a bayesian belief structuration showed the difficulty to model the uncertainty in the fusion process. This is probably due to the lack of scientific knowledge about the influence of the biophysical and climatic parameters on the mapped forest stands and to the necessity to model specifically the uncertainty for each source. Our work showed concrete improvement when mapping forest stands with DST which is encouraging to continue explorating the fundamental principle of the proposed hybrid decision rule. This means a particular focus on the difference between the fused masses of each potential class after the fusion, to choose the best hypothesis