Interband Consistency-Driven Structural Subspace Clustering for Unsupervised Hyperspectral Band Selection

In the classification applications of hyperspectral remote sensing images (HSIs), band selection is crucial for mitigating the curse of dimensionality while preserving the intrinsic physical information within HSIs. Although clustering-based band selection methods are widely applied, they often overlook the inherent physical properties of hyperspectral images. Such approaches typically operate in raw high-dimensional space, which is susceptible to noise and redundancy. This results in generated band combinations that fail to adequately characterize the spectral features of the underlying materials, leading to suboptimal band-grouping schemes. To address this, we propose a novel Interband Consistency-Constrained Structural Subspace Clustering (ICC-SSC) method. The core assumption is that the spectral characteristics of land cover inherently reside within a low-dimensional subspace, where bands within this subspace should exhibit strong physical consistency, which means that the spectral signatures of land covers show significant similarity across these bands. Driven by this physical interpretation, our method innovates in two ways. Specifically, we employ the l1,2 norm in the self-representation model to discover the inherent grouping structure of the bands. This enforces a small set of common, representative basis bands to reconstruct others, effectively identifying the most physically informative bands that anchor these material-specific subspaces. In addition, we incorporate a total variance (TV) regularization term into the proposed model to capture the smoothing characteristics between adjacent bands. This physics-based constraint enhances the consistency of representations among adjacent bands, ensuring that subspace representations across all bands maintain well-structured coherence. An efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is derived to solve the proposed model. Extensive experiments on three real HSIs demonstrate that ICC-SSC significantly outperforms state-of-the-art methods.