Graph Laplacian Regularization and Local Collaborative Sparse Regression Based on Superpixel Segmentation for Hyperspectral Imagery

Spatial-regularized spectral unmixing has achieved great progress and attracted widespread attention for addressing homogeneous regions with identical spectral characteristics. In this paper, a new hyperspectral unmixing algorithm with graph Laplacian regularization and local collaborative sparse regression is proposed, based on superpixel segmentation, namely GLCGSU. Considering mixed pixels in homogeneous areas have similar endmembers and abundances, we utilize superpixel image clustering (SLIC) to cluster similar pixels, leveraging boundary information for uniform area extraction. Spatial similarity is investigated via graph Laplacian regularization. Meanwhile, we apply local collaborative weighted sparse regression to achieve abundance matrix sparsity. Experimental results demonstrates the effectiveness of the proposed method both on simulated and real data, proving its superiority for hyperspectral unmixing.