AM-GMAE: Adaptive Manifold Graph Masked Autoencoder for Multi-view Remote Sensing Data Clustering

Multi-view clustering has emerged as a mainstream unsupervised paradigm for analyzing remote sensing data by jointly exploiting complementary multi-sensor observations. Graph Masked Autoencoders (GMAEs), which learn informative graph representations through self-supervised reconstruction, offer a promising solution for these multi-view scenarios. Yet, effectively extending GMAEs to heterogeneous multi-view remote sensing data remains challenging for three main reasons: i) existing masking strategies ignore node importance, risking trivial cross-view reconstruction shortcuts; ii) initial similarity-based graphs suffer from noise and class ambiguity, distorting the underlying manifold during message passing; and iii) forced alignment of heterogeneous latent spaces often induces negative transfer. To address these issues, we propose the Adaptive Manifold Graph Masked Autoencoder (AM-GMAE). Specifically, AM-GMAE introduces a Clustering-Saliency Adversarial Mask module to identify and adaptively mask nodes with high clustering-uncertainty sensitivity across correlated views, thereby increasing reconstruction difficulty and reducing reliance on trivial cross-view cues. Furthermore, we develop a Dynamic Graph Purification and Topology Optimization module to prune misleading edges and refine the graph manifold using structural information-theoretic constraints. Finally, a Multi-marginal Optimal Transport module with Wasserstein barycenter estimation is employed to align heterogeneous representations through a shared Wasserstein semantic geometry without imposing rigid pairwise coupling. Extensive experiments on four widely used remote sensing benchmarks demonstrate that AM-GMAE outperforms SOTA methods.