Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.