Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint

Abstract

Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image. Sparse regression techniques have been widely used to solve this optimization problem, since the number of materials present in a scene is usually small. However, the high mutual coherence of library signatures negatively affects the sparse unmixing performance. To cope with this challenge, a new algorithm called spectral-spatial low-rank sparse unmixing (SSLRSU) is established. In this article, the double weighting factors under the $\ell _{1}$ framework aim to improve the row sparsity of the abundance matrix and the sparsity of each abundance map. Meanwhile, the low-rank regularization term exploits the low-dimensional structure of the image, which makes for accurate endmember identification from the spectral library. The underlying optimization problem can be solved by the alternating direction method of multipliers efficiently. The experimental results, conducted by using both synthetic and real hyperspectral data, uncover that the proposed SSLRSU strategy can get accurate unmixing results over those given by other advanced sparse unmixing strategies.

Keywords

• Hyperspectral imaging
• low-rank constraint
• sparse unmixing
• weighted sparse regression