Improving Joint Sparse Hyperspectral Unmixing by Simultaneously Clustering Pixels According To Their Mixtures

Abstract

In this paper we propose a novel hierarchical Bayesian model for sparse regression problem to use in semi-supervised hyperspectral unmixing which assumes the signal recorded in each hyperspectral pixel is a linear combination of members of the spectral library contaminated by an additive Gaussian noise. To effectively utilizing the spatial correlation between neighboring pixels during the unmixing process, we exploit a Markov random field to simultaneously group pixels to clusters which are associated to regions with homogeneous mixtures in a natural scene. We assume Sparse fractional abundances of members of a cluster to be generated from an exponential distribution with the same rate parameter. We show that our method is able to detect unconnected regions which have similar mixtures. Experiments on synthetic and real hyperspectral images confirm the superiority of the proposed method compared to alternatives.