Implicit Doubly Stochastic Graph-Based Subspace Clustering for Hyperspectral Band Selection

Band selection plays a critical role in reducing data redundancy of hyperspectral images (HSIs), where subspace clustering-based methods have shown remarkable potential, owing to the effective extraction of low-dimensional representations. Despite conducting band grouping competently, they not only overlook preserving original affinity structures but also require a manual graph normalization step, which could lead to suboptimal performance. To solve these issues, we propose a novel band selection method termed implicit doubly stochastic graph-based subspace clustering (IDSGC), facilitating end-to-end band affinity graph construction. First, a latent bipartite graph modeling (LBGM) module is designed to decompose the inherent band similarities into a bipartite graph, while mitigating the interference of noise links. Second, a novel implicit learning mechanism (ILM) of a doubly stochastic graph is presented to refine graph structure and low-dimensional representations simultaneously, which seamlessly integrates the LBGM with subspace learning. Finally, a probability-constrained affinity graph that is implicitly normalized during optimization is generated, which can be directly used for band clustering without additional postprocessing. Extensive experiments demonstrate that IDSGC outperforms several state-of-the-art band selection methods.