LAUSR

Due to the redundancy and sparsity of hyperspectral data, sparse representation (SR) has proven to be well-suited for hyperspectral band selection (BS). Moreover, graph regularizers can effectively incorporate local structural information of the data to improve the solution of SR. However, existing unsupervised BS approaches typically consider only a simple graph based on a single spectral metric. In contrast, the hypergraph can capture the multiple adjacencies of the bands and has significant advantages. This letter proposes a hypergraph-regularized self-representation (HyGSR) model for BS. HyGSR is innovative in that it jointly combines the spectral similarity and band index as a new similarity metric to rationalize the local structure of bands extracted by hypergraph, while using a robust $l_{2,1}$ -norm to exploit the sparse properties of the data for BS. Experimental results on four real hyperspectral scenarios verify that HyGSR outperforms other competitors with high stability and usability.