Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales

Binary partition tree for hyperspectral imaging

• Auteur / Autrice : Silvia Valero Valbuena
• Direction : Jocelyn Chanussot, Philippe Salembier
• Type : Thèse de doctorat
• Discipline(s) : Signal, image, paroles, télécoms
• Date : Soutenance le 09/12/2011
• Etablissement(s) : Grenoble en cotutelle avec Universitat politècnica de Catalunya - BarcelonaTech
• Ecole(s) doctorale(s) : École doctorale électronique, électrotechnique, automatique, traitement du signal (Grenoble ; 199.-....)
• Partenaire(s) de recherche : Laboratoire : Grenoble Images parole signal automatique
• Jury : Président / Présidente : Jean Serra
• Jury : Examinateurs / Examinatrices : Jesús Angulo López, Hugues Talbot, Carlos María López Martínez
• Jury : Rapporteurs / Rapporteuses : Antonio Plaza, Josiane Zerubia

Mots clés

Mots clés contrôlés

Télédétection

Imagerie hyperspectrale

Mots clés libres

Arbre binaire de partitions

Imagerie hyperspectrale

Filtrage

Segmentation

Teledetection

Partition tree

Hyperspectral imagery

Filtering

Segmentation

Remote sensing

Résumé

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.