Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

• URL: http://arxiv.org/abs/2007.14033v2
• Date: Sat, 12 Sep 2020 13:12:49 GMT
• Title: Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing
• Authors: Taner Ince
• Abstract summary: We use a superpixel segmentation algorithm to extract the homogeneous regions by considering the image boundaries. We first extract the homogeneous regions, which are called superpixels, then a weighted graph in each superpixel is constructed by selecting $K$-nearest pixels in each superpixel. The spatial similarity is investigated using graph Laplacian regularization.
• Score: 1.14219428942199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for sparse hyperspectral unmixing method. Since it is likely to find spectrally similar pixels in a homogeneous region, we use a superpixel segmentation algorithm to extract the homogeneous regions by considering the image boundaries. We first extract the homogeneous regions, which are called superpixels, then a weighted graph in each superpixel is constructed by selecting $K$-nearest pixels in each superpixel. 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 using graph Laplacian regularization. Sparsity regularization for abundance matrix is provided using a weighted sparsity promoting norm. Experimental results on simulated and real data sets show the superiority of the proposed algorithm over the well-known algorithms in the literature.

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