Exploitation of Sparsity for Hyperspectral Target Detection. (Exploitation de la parcimonie pour la détection de cibles dans les images hyperspectrales)

Le titre de cette these de doctorat est forme de trois mots cles: parcimonie, image hyperspectrale, et detection de cibles. La parcimonie signifie generalement « petit en nombre ou quantite, souvent repartie sur une grande zone ». Une image hyperspectrale est constituee d'une serie d'images de la meme scene spatiale, mais prises dans plusieurs dizaines de longueurs d'onde contigues et tres etroites, qui correspondent a autant de "couleurs". Lorsque la dimension spectrale est tres grande, la detection de cibles devient delicate et caracterise une des applications les plus importantes pour les images hyperspectrales. Le but principal de cette these de doctorat est de repondre a la question « Comment et Pourquoi la parcimonie peut-elle etre exploitee pour detecter de cibles dans les images hyperspectrales ? ». La reponse a cette question nous a permis de developper des methodes de detection de cibles prenant en compte l'heterogeneite de l'environnement, le fait que les objets d'interet sont situes dans des parties relativement reduites de l'image observee et enfin que l'estimation de la matrice de covariance d'un pixel d'une image hyperspectrale peut etre compliquee car cette matrice appartient a un espace de grande dimension. Les methodes proposees sont evaluees sur des donnees synthetiques ainsi que reelles, dont les resultats demontrent leur efficacite pour la detection de cibles dans les images hyperspectrales.

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