ENDMEMBER DICTIONARY BASED HYPERSPECTRAL IMAGE UNMIXING

The existence of mixed pixels pose difficulty for many hyperspectral image applications. In this paper, we propose an endmember dictionary based algorithm for hyperspectral image unmixing. A material class based endmember dictionary pre-trained on a standard spectral library is used as the set of endmembers for unmixing. A K-SVD based sparse decomposition algorithm is adopted to capture the abundances of the endmembers. By training the endmembers from standard spectral library, typicality of the endmembers are improved and correlations between the endmembers are reduced. Experimental results show that the proposed algorithm improves performances for both simulated and real data, especially in low SNR cases.