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Superpixel-Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing
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DOI：10.1109/LGRS.2020.3027055.png)
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摘要
En 中文
An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for the 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, and then, a weighted graph in each superpixel is constructed by selecting 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 the graph Laplacian regularization. Sparsity regularization for an 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.
Keyword:
Sparse matrices
Laplace equations
Hyperspectral imaging
Image segmentation
Libraries
Data mining
Abundance estimation
graph Laplacian
sparse unmixing (SU)
superpixel
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16.4
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5.1K
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