Spectral-spatial joint sparsity unmixing of hyperspectral data using overcomplete dictionaries

Sparse spectral unmixing can be modeled as a linear combination of endmembers contained in an overcomplete dictionary weighted by the corresponding sparse abundance vector. This method exploits the fact that there is only a small number of endmembers inside a pixel compared to the overcomplete endmember spectral dictionary. Since the information contained in hyperspectral pixels is often spatially correlated, in this work we propose to jointly estimate the sparse abundance vectors of neighboring hyperspectral pixels within a local window exploiting joint sparsity with common and noncommon endmembers. To demonstrate the efficiency of our framework, we perform experiments using both simulated and real hyperspectral data.