Sparse hyperspectral unmixing algorithm supported by sparse difference prior information

doi:10.11947/j.AGCS.2020.20190205

Abstract

Abstract: Spectral library-based hyperspectral sparse unmixing technology has received attention in recent years, which uses spectral samples in the spectral library as endmembers and transforms the unmixing problem into a sparse representation problem. However, due to differences in the measurement environment, the actual endmembers of the hyperspectral image to be unmixed tend to differ from the corresponding spectral signatures in the spectral library. In this paper, an unmixing algorithm named spectral difference sparse constrained collaborative sparse regression is proposed. Firstly, we assume that the spectral differences have sparse property, and a spectral library correction model is established, which can make the spectral library be adaptively adjusted during the unmixing process; Then, the spectral library correction model is combined with the collaborative sparse regression unmixing model to establish a sparse unmixing model considering spectral differences; Finally, an iterative optimization solution based on the alternating direction method of multipliers is given. Synthetic and real hyperspectral data are used to verify the performance of different algorithms. The results show that the proposed algorithm is more effective than the compared algorithms in the presence of spectral library mismatches.

Key words: hyperspectral image, unmixing, sparse regression, spectral difference, spectral library c orrection

CLC Number:

P237

ZHANG Zuoyu, LIAO Shouyi, SUN Dawei, ZHANG Hexin, WANG Shicheng. Sparse hyperspectral unmixing algorithm supported by sparse difference prior information[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(8): 1032-1041.

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