Résumé non communiqué par le doctorant. / The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation:the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpretedas a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeedsin presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusionrelations of the regions in the scene. Based on region-merging techniques, the construction of BPTis investigated in this work by studying hyperspectral region models and the associated similaritymetrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniqueson it. The application-dependent processing of BPT is generally implemented through aspecific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detectionand classification of hyperspectral imagery. Experimental results on various hyperspectraldata sets demonstrate the interest and the good performances of the BPT representation.

La segmentation est une tâche cruciale en analyse d’images. L’évolution des capteurs d’acquisition induit de nouvelles images de résolution élevée, contenant des objets hétérogènes. Il est aussi devenu courant d’obtenir des images d’une même scène à partir de plusieurs sources. Ceci rend difficile l’utilisation des méthodes de segmentation classiques. Les approches de segmentation hiérarchiques fournissent des solutions potentielles à ce problème. Ainsi, l’Arbre Binaire de Partitions (BPT) est une structure de données représentant le contenu d’une image à différentes échelles. Sa construction est généralement mono-critère (i.e. une image, une métrique) et fusionne progressivement des régions connexes similaires. Cependant, la métrique doit être définie a priori par l’utilisateur, et la gestion de plusieurs images se fait en regroupant de multiples informations issues de plusieurs bandes spectrales dans une seule métrique. Notre première contribution est une approche pour la construction multicritère d’un BPT. Elle établit un consensus entre plusieurs métriques, permettant d’obtenir un espace de segmentation hiérarchique unifiée. Par ailleurs, peu de travaux se sont intéressés à l’évaluation de ces structures hiérarchiques. Notre seconde contribution est une approche évaluant la qualité des BPTs en se basant sur l’analyse intrinsèque et extrinsèque, suivant des exemples issus de vérités-terrains. Nous discutons de l’utilité de cette approche pour l’évaluation d’un BPT donné mais aussi de la détermination de la combinaison de paramètres adéquats pour une application précise. Des expérimentations sur des images satellitaires mettent en évidence la pertinence de ces approches en segmentation d’images. / Segmentation is a crucial task in image analysis. Novel acquisition devices bring new images with higher resolutions, containing more heterogeneous objects. It becomes also easier to get many images of an area from different sources. This phenomenon is encountered in many domains (e.g. remote sensing, medical imaging) making difficult the use of classical image segmentation methods. Hierarchical segmentation approaches provide solutions to such issues. Particularly, the Binary Partition Tree (BPT) is a hierarchical data-structure modeling an image content at different scales. It is built in a mono-feature way (i.e. one image, one metric) by merging progressively similar connected regions. However, the metric has to be carefully thought by the user and the handling of several images is generally dealt with by gathering multiple information provided by various spectral bands into a single metric. Our first contribution is a generalized framework for the BPT construction in a multi-feature way. It relies on a strategy setting up a consensus between many metrics, allowing us to obtain a unified hierarchical segmentation space. Surprisingly, few works were devoted to the evaluation of hierarchical structures. Our second contribution is a framework for evaluating the quality of BPTs relying both on intrinsic and extrinsic quality analysis based on ground-truth examples. We also discuss about the use of this evaluation framework both for evaluating the quality of a given BPT and for determining which BPT should be built for a given application. Experiments using satellite images emphasize the relevance of the proposed frameworks in the context of image segmentation.