Sparse Unmixing for Hyperspectral Image Based on Spatial Homogeneous Analysis

Abstract

Abstract:

Endmember abundance of hyperspectral imagery is of notable sparsity and distributing smoothness in spatial space. According to these two properties, a sparse unmixing algorithm based on imagery spatial homogeneity analysis is proposed in this paper. Firstly, homogeneity index is calculated by imagery spatial homogeneity analysis. Then the spatial regularizers of the sparse regression unmixing model are weighted according to the homogeneity index. This model can reflect the spatial distribution complexity of endmember abundance and make the unmixing process more effective. Experiments on both simulated and real hyperspectral data show that this algorithm well keeps unmixing abundance sparsity and spatially smoothness with good noise immunity and promotes entire unmixing accuracy.

Key words: hyperspectral imagery, spectral unmixing, sparse regression, homogeneity analysis

CLC Number:

P237

References

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