Spatial-Spectral Hypergraph-Based Unsupervised Band Selection for Hyperspectral Remote Sensing Images

Unsupervised band selection identifies informative bands in hyperspectral images (HSIs) without prior labeling, reducing spectral redundancy. Besides spectral information, the spatial-spectral structures of HSIs can be exploited jointly to select more valuable bands and reduce the impact of noises. In this article, we present a novel spatial-spectral hypergraph-based unsupervised band selection (SSHUBS) method. First, since hypergraphs are effective in expressing complex high-order relations among pixels and bands, a spatial hypergraph is built using pixels within local spatial homogeneous regions, and a spectral hypergraph is built using bands in clusters generated by an over-clustering strategy. The two hypergraphs could embed the HSIs' spatial and spectral information into the band selection process, respectively. Second, two normalized hypergraph Laplacian matrices are generated to reformulate the optimization problem of the classical sparse self-representation (SR) band selection framework. Combining the obtained coefficient matrix with the cluster sizes to rank each spectral band, representative bands are selected. Finally, experiments conducted on hyperspectral remote sensing data verify the effectiveness of the proposed method in selecting bands to improve the classification accuracy compared to state-of-the-art methods.