Superpixel-Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

An efficient spatial regularization method using superpixel segmentation and graph Laplacian is proposed for the sparse hyperspectral unmixing method. Since it likely to find spectrally similar pixels in a homogeneous region, we use algorithm extract regions by considering image boundaries. We first regions, which are called superpixels, then, weighted each constructed selecting $K$ -nearest superpixel. Each node represents spectrum of pixel, edges connect inside The similarity investigated regularization. Sparsity an abundance matrix provided sparsity promoting norm. Experimental results on simulated real data sets show superiority over well-known algorithms literature.