HYPERSPECTRAL IMAGE SEGMENTATION AND UNMIXING USING HIDDEN MARKOV TREES

This paper is concerned with joint Bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors. We hypothesize that hyperspectral images are composed of two types of regions. For the first type, the material proportions of adjacent pixels are similar and can be jointly characterized by a single vector, and in the second, neighboring pixels have very different abundances and are characterized by unique mixing proportions. Using this hypothesis we propose a new unmixing algorithm which simultaneously segments the image into such regions and performs unmixing. The experimental results show that the new algorithm can lead to improved MSE of both the extracted endmembers and the estimated abundances in low SNR cases.