Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for sparse hyperspectral unmixing method. A superpixel is defined as a group of structured neighboring pixels which constitutes a homogeneous region. First, we segment the hyperspectral image into many superpixels. Then, a weighted graph in each superpixel is constructed. Each node in the graph represents the spectrum of a pixel and edges connect the similar pixels inside the superpixel. The spatial similarity is investigated in each superpixel using graph Laplacian regularization. A weighted sparsity promoting norm is included in the formulation to sparsify the abundance matrix. Experimental results on simulated and real data sets show the superiority of the proposed algorithm over the well-known algorithms in the literature.