Implicit Doubly Stochastic Graph-Based Subspace Clustering for Hyperspectral Band Selection (opens in new tab)

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 selec...