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BESIC Nikola

Séparation aveugle des sources polarimétriques en télédétection RSO satellitaire à trés haute résolution spatiale

 

Directeur de thèse :     Jocelyn CHANUSSOT

Co-encadrant :     Gabriel VASILE

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

Spécialité : Signal, image, parole, télécoms

Structure de rattachement : Grenoble-INP

Établissement d'origine : INP-PHELMA

Financement(s) : Contrat doctoral ; contrat à durée déterminée

 

Date d'entrée en thèse : 01/10/2011

Date de soutenance : 21/11/2014

 

Composition du jury :
M. Predrag MIRANOVIC, Professeur, Université du Monténégro, Président
M. Laurent FERRO-FAMIL, Professeur, Université de Rennes I, Rapporteur
M. Philippe RÉFRÉGIER, Professeur, École Centrale de Marseille, Rapporteur
Mme Marie CHABERT, Professeur, INP Toulouse, Examinateur
M. Antonio PLAZA, Professeur, Université d''Estrémadure, Examinateur
M. Guy D''URSO, Ingénieur de recherche, R&D EDF, Membre invité
M. Gabriel VASILE, Chargé de recherche, CNRS, Encadrant
M. Jocelyn CHANUSSOT, Professeur, Grenoble INP, Directeur de thèse
M. Srdjan STANKOVIC, Professeur, Université du Monténégro, Co-Directeur de thèse

 

Résumé : This thesis comprises two research axes. The first, being rather methodological, consists of our efforts to answer some of the open questions in the POLSAR community, while the latter is sooner related to the specific application - the remote sensing of snow. Following the alternative statistical modelling of highly textured multivariate SAR datasets by means of SIRV model, we propose the appropriate assessment of, otherwise assumed, circularity and sphericity parameters. The last is coupled with the spherical symmetry test, forming a method for the evaluation of SIRV statistical model suitability in the context of POLSAR data. Given the rejection rate, challenging circularity and sphericity appears to be justified, while SIRV model pertinence must be doubted in the regions characterized by strong deterministic scattering. Further on, as the highlight of this thesis, we propose a polarimetric incoherent target decomposition, based on ICA and founded on the hypothesis of non-Gaussianity of POLSAR clutter. By exploiting the information contained in the higher statistical orders, this decomposition provides at the output a set of mutually independent (rather than only decorrelated), non-orthogonal target vectors. Unlike the first dominant component, which is nearly identical to the one estimated by the conventional ICTD counterpart, the second dominant component differs significantly, which, as we anticipate, represents an additional potential for the POLSAR datasets interpretation. In the applied context, the first presented contribution would be a stochastic approach in snow mapping by means of multitemporal SAR datasets. The most notable supplements of the presented method to the ensemble of change detection techniques in snow mapping are the plausibly modified assumption of the wet/dry snow backscattering ratio and implicitly introduced spatial correlation between wet snow areas, achieved by directly implicating local speckle statistics in the decision process. Finally, we present the non-autonomous method for SWE spatial estimation, based on optical datasets. By successfully involving the remote sensing datasets in the calibration of the external SWE model, we seek to demonstrate the utility and the necessity of the former in the snow pack monitoring.


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