Multiview Multilevel Robust Deep Subspace Clustering for Hyperspectral Band Selection

Subspace clustering-based band selection (BS) have achieved significant progress. However, traditional approaches focus solely on the linear relationships among bands, neglecting important nonlinear information. Moreover, single-view data inherently limits the exploitation of complementary latent features across different views. To address the above issues, this paper proposes a novel multi-view multi-level robust deep subspace clustering method for BS (MRDSC). First, we construct spectral, spatial, and texture views to provide rich and complementary feature representations for subspace clustering. Further, a multi-level deep subspace clustering network is employed to deeply capture both global and local nonlinear relationships within the self-representation coefficient matrix of each view, while Robust Principal Component Analysis is applied to obtain a clean coefficient matrix. Finally, a self-aligned contrastive learning mechanism is introduced to enhance the consistency of positive samples and the discriminability of negative samples, effectively integrating complementary information across views and reinforcing feature consistency. Experimental results on three public datasets demonstrate that the proposed method exhibits high stability and effectiveness.