Discriminative Anchor Learning for Hyperspectral Image Clustering

Anchor-based clustering algorithms for hyperspectral image (HSI) have alleviated the computational burden and become a prominent research direction in remote sensing. The performance results of these methods are heavily influenced by the quality of the anchors. However, existing anchor-based methods directly utilize the learned anchors for clustering, ignoring the distribution consistency between anchors and samples. This leads to the degradation of anchor quality and suboptimal clustering performance. To address the problem, we propose a discriminative anchor-based hyperspectral image clustering algorithm. Specifically, by sharing the coefficient matrix among anchors and that between samples and anchors, the proposed method explicitly takes into account the distribution of samples and anchors, enabling the learned anchors to better describe the samples. We further impose low-rank and probabilistic constraints on the consensus coefficient matrix, which effectively captures latent structural information and enhances the anchors’ discriminative ability. Extensive experimental results on public HSI benchmarks demonstrate the superiority and effectiveness of the proposed method.