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Bayesian signal processing techniques for hyperspectral image unmixing

In this dissertation the problem of semi-supervised spectral unmixing is studied. We describe a framework of Bayesian techniques that take into account the special characteristics of hyperspectral data and exploit the prior knowledge of the model’s constraints. The assumption of sparsity on the unmixing process is introduced, which leads to the development of efficient schemes for spectral unmixing. Besides, a novel method for Bayesian inference is proposed, termed BI-ICE, which is computationally efficient, and can be considered as a first-moments approximation to variational inference methods. Experimental results conducted both on simulated and real hyperspectral data are presented that illustrate the robust estimation performance of the proposed sparsityinducing methods.