Multi-view Graph Clustering with Dual Structure Awareness for Remote Sensing Data

Multi-view clustering plays a pivotal role in remote sensing image analysis, where graph neural network-based methods have demonstrated remarkable potential by modeling data as graphs. However, existing efforts, which construct remote sensing graphs using fixed rules (e.g., K-nearest neighbors), inevitably introduce noisy edges and increase the risk of heterogeneous information diffusion, leading to inferior clustering performance. Although recent works attempt to address this issue by refining the structure, they are designed for single-view data and struggle to extend to multi-view scenarios. To bridge this gap, we propose a dual structure awareness multi-view graph clustering method named DSMVGC, which generates two distinct structures for each view through explicit and implicit perspectives. Specifically, in our method, the learning processes of structure refinement and clustering are alternately optimized to mutually enhance each other. On one hand, the explicit structure updates the topology based on inter-cluster relationships, while the implicit structure captures latent relationships not covered by the explicit structure through adversarial learning. On the other hand, the refined structures not only facilitate homogeneous message passing but also serve as prior knowledge to guide the contrastive loss, thereby enhancing the discriminability of representations for accurate clustering. Extensive experiments on five multi-view remote sensing datasets validate the effectiveness of DSMVGC.