非凸稀疏低秩约束的高光谱解混方法

doi:10.3969/j.issn.1001-2400.2016.06.020

Abstract

Abstract:

Aiming at the row sparse feature of the abundance matrix of hyperspectral mixed pixels, a hyperspectral unmixing method based on the non-convex sparse and low-rank constraints is presented. The non-convex sparse representation and non-convex low-rank models are first constructed, which take the non-convex p-norm of the abundance matrix as the sparse constraint and the non-convex p-norm of the singular values of the abundance matrix as the low-rank constraint. Then the low-rank prior and the sparse prior are jointly utilized to construct a non-convex minimization model. An augmented lagrange alternating minimization method is proposed to solve the unmixing model, the compound regularization problem is decomposed into multiple single regularization problems solved by the variable separation method. Experimental results demonstrate that the proposed method outperforms the greedy algorithm and the convex algorithms with a better spectral unmixing accuracy, and is suitable for high signal-to-noise ratio hyperspectral data.

Key words: image processing, sparse unmixing, sparse representation, low-rank, convex optimization

KONG Fanqiang;BIAN Chending;LI Yunsong;GUO Wenjun. Hyperspectral unmixing method based on the non-convex sparse and low-rank constraints[J].Journal of Xidian University, 2016, 43(6): 116-121.

References

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Hyperspectral unmixing method based on the non-convex sparse and low-rank constraints

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