Estimation et détection en imagerie hyperspectrale : application aux environnements côtiers. (Estimation and detection in hyperspectral imagery : application in coastal environments)

Cette these aborde des problematiques d'estimation et de detection supervisee en imagerie hyperspectrale, appliquees ici aux environnements cotiers. Des modeles bathymetriques de reflectance sont utilises afin de representer l'influence de la colonne d'eau sur la lumiere incidente. Differents parametres sont dits optiquement actifs et agissent sur le spectre de reflectance (phytoplancton, matiere organique dissoute coloree. . . ). Nous proposons d'adopter une nouvelle approche statistique pour estimer ces parametres, traditionnellement retrouves par inversion des modeles physiques. Differentes methodes telles que l'estimation du maximum de vraisemblance et du maximum a posteriori, ainsi que le calcul des bornes de Cramer-Rao, sont implementees avec succes sur les donnees synthetiques et reelles. Par ailleurs, nous adaptons les filtres supervises couramment utilises au contexte de la detection de cibles immergees. Dans le cas ou les parametres caracteristiques de la colonne d'eau sont inconnus, nous developpons un nouveau filtre issu du test du rapport de vraisemblance generalise permettant la detection sans aucune connaissance a priori sur ces parametres

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