PURE ENDMEMBER EXTRACTION USING SSR FOR HYPERSPECTRAL IMAGERY

This manuscript proposes a symmetric sparse representation (SSR) method to extract pure endmembers from Hyperspectral imagery (HSI). The SSR assumes that the desired endmembers and all the HSI pixels can be sparsely represented by each other and it formulates the endmember extraction problem into finding archetypes in the minimal convex hull of the HSI data. The optimization program of SSR is solved by a simple projected gradient algorithm and the endmembers are initialized with the vector quantization scheme. Preliminary results on the popular Urban HSI data infer that the SSR performs better than several state-of-the-art methods (VCA, NFINDER, AVMAX, SVMAX, XRAY, OSP and H2NMF).