Abstract / truncated to 115 words (read the full abstract)

Spectral unmixing is one the major issues arising when analysing hyperspectral images. It consists of identifying the macroscopic materials present in a hyperspectral image and quantifying the proportions of these materials in the image pixels. Most unmixing techniques rely on a linear mixing model which is often considered as a first approximation of the actual mixtures. However, the linear model can be inaccurate for some specific images (for instance images of scenes involving multiple reflections) and more complex nonlinear models must then be considered to analyse such images. The aim of this thesis is to study new nonlinear mixing models and to propose associated algorithms to analyse hyperspectral images. First, a post-nonlinear model is investigated ... toggle 5 keywords

hyperspectral imagery – spectral unmixing – bayesian estimation – nonlinear models – mcmc methods

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