Dual-View Structural Similarity Subspace Clustering for Hyperspectral Band Selection

Band selection (BS) is a vital technique for improving efficiency of hyperspectral image (HSI) processing. This letter proposes a dual-view structural similarity subspace clustering model (DVS3C) for BS. Traditional low-rank subspace clustering (LRSC) methods rely solely on single-view data (e.g., original HSI), potentially leading to the loss of critical information (e.g., spatial structures) and insufficient exploitation of the multi-dimensional features of HSI for optimal BS. To do so, DVS3C constructs a spatial view alongside the spectral view, leveraging global spectral-spatial information through subspace clustering to achieve complementary advantages between views. Besides, to overcome LRSC’s limitations in capturing band local structure, DVS3C introduces a structural similarity matrix to deeply exploit intraview neighborhood relationships of bands, further reducing band redundancy. Ultimately, an adaptive dual-view fusion strategy that iteratively optimizes a consensus matrix while dynamically adjusting the contribution of each view is designed to ensure view consistency. Experimental results on four public datasets demonstrate its remarkable stability and superiority. The source code is available at https://github.com/ydk0912/DVS3C.