Hypergraph-Regularized Low-Rank Subspace Clustering Using Superpixels for Unsupervised Spatial-Spectral Hyperspectral Classification

Jinhuan Xu

doi:10.1109/LGRS.2020.2985981

Hypergraph-Regularized Low-Rank Subspace Clustering Using Superpixels for Unsupervised Spatial-Spectral Hyperspectral Classification

2021-05-01

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DOI：10.1109/LGRS.2020.2985981

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Low-rank subspace representations have been observed to be well-suited to hyperspectral imagery, which tends to have a global structure composed of a small number of ground-cover signatures, and additional graph-based regularization can further incorporate local information. However, in the context of unsupervised classification, existing approaches typically limit consideration to simple graphs built on spectral information alone. In contrast, a hypergraph-based low-rank subspace clustering is proposed to capture a more complex manifold structure. In addition, basing the hypergraph on a superpixel segmentation of the image exploits structure that is meaningful both spatially as well as spectrally. The experimental results reveal performance for the proposed superpixel-hypergraph approach superior to that of competing techniques representative of several prominent classes of unsupervised classification for hyperspectral imagery.

Keywords:

Hyperspectral imaging

Erbium

Laplace equations

Image segmentation

Image edge detection

Manifolds

Clustering

hypergraph

hyperspectral imagery

low-rank decomposition

superpixel

Hypergraph-Regularized Low-Rank Subspace Clustering Using Superpixels for Unsupervised Spatial-Spectral Hyperspectral Classification

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