Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

TL;DR

This paper introduces a novel hyperspectral unmixing method that combines superpixel segmentation with graph Laplacian regularization to improve spatial regularization and sparsity, demonstrating superior performance on simulated and real datasets.

Contribution

It proposes a new spatial regularization technique using superpixels and graph Laplacian for sparse hyperspectral unmixing, enhancing accuracy over existing methods.

Findings

Outperforms well-known algorithms on simulated data

Effective in real-world hyperspectral datasets

Improves spatial regularization and sparsity in unmixing

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.