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Theses

Méthodes avancées de séparation de sources applicables aux mélanges linéaires-quadratiques

Abstract : In this thesis, we were interested to propose new Blind Source Separation (BSS) methods adapted to the nonlinear mixing models. BSS consists in estimating the unknown source signals from their observed mixtures when there is little information available on the mixing model. The methodological contribution of this thesis consists in considering the non-linear interactions that can occur between sources by using the linear-quadratic (LQ) model. To this end, we developed three new BSS methods. The first method aims at solving the hyperspectral unmixing problem by using a linear-quadratic model. It is based on the Sparse Component Analysis (SCA) method and requires the existence of pure pixels in the observed scene. For the same purpose, we propose a second hyperspectral unmixing method adapted to the linear-quadratic model. It corresponds to a Non-negative Matrix Factorization (NMF) method based on the Maximum A Posteriori (MAP) estimate allowing to take into account the available prior information about the unknown parameters for a better estimation of them. Finally, we propose a third BSS method based on the Independent Component Analysis (ICA) method by using the Second Order Statistics (SOS) to process a particular case of the linear-quadratic mixture that corresponds to the bilinear one.
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Lina Jarboui. Méthodes avancées de séparation de sources applicables aux mélanges linéaires-quadratiques. Traitement du signal et de l'image [eess.SP]. Université Paul Sabatier - Toulouse III; Université de Sfax (Tunisie), 2017. Français. ⟨NNT : 2017TOU30295⟩. ⟨tel-01988590⟩

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