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Hyperspectral image processing and representation using Binary Partition Trees.


Directeur de thèse :     Jocelyn CHANUSSOT

École doctorale : Electronique, electrotechnique, automatique, traitement du signal (EEATS)

Spécialité : Signal, image, parole, télécoms

Structure de rattachement : Université Grenoble Alpes

Établissement d'origine : Université Polytechnique de Catalogne

Financement(s) : allocation MENRT ; ATER


Date d'entrée en thèse : 01/10/2008

Date de soutenance : 09/12/2011


Composition du jury :
Zerubia, Josiane
Plaza, Antonio
Serra, Jean
Talbot, Hugues
Angulo, Jesus
Lopez-Martinez, Carlos


Résumé : The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This representation can be interpreted as a set of hierarchical regions stored in a tree structure. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. Once the BPT is constructed,the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific 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 detection and classification of hyperspectral imagery. Experimental results on various hyper spectral data sets demonstrate the interest and the good performances of the BPT representation.

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