Structured Sparsity Characteristics in Hyperspectral Unmixing

Mixed pixels of hyperspectral image own very high sparsity if they are linearly represented by endmembers of aprior spectral library. Hence the sparse regression framework has been introduced to solve the linear spectral unmixing problem and produced sparse fractional abundances. But because of the high coherence of spectral library and noise disturbance, the solution could not be as sparse as real. Structure sparsity is introduced to the sparse regression based unmixing method to mitigate the disturbances and make the solution sparser and more accurate. This paper gives an overview of the structure feature of hyperspectral image such as spatial smoothness, collaborative sparsity and group structure of spectral library as well as corresponding structure sparsity based hyperspectral unmixing methods. Furthermore some ideas of future work on structure sparsity in the unmixing of hyperspectral image are proposed at last.