A Unified Unsupervised Framework for Multimodal Remote Sensing Change Detection

Unsupervised multimodal remote sensing change detection is challenging due to severe cross-modality discrepancies and unreliable pixel-wise correspondence. Existing methods usually treat cross-modal alignment and change discrimination as separate processes, which may lead to error accumulation and limited robustness under strong modality differences. In this paper, we propose a unified unsupervised framework that tightly couples structural alignment and change discrimination. A non-local graph alignment (NLG) module is introduced to establish structure-preserving cross-modal correspondence by modeling non-local spatial relationships. Meanwhile, a global-local state (GLS) discrimination module based on state-space modeling is designed to capture both long-range dependency patterns and fine-grained local variations of changes. The two modules are iteratively optimized in an end-to-end manner, eliminating the reliance on pseudo-label generation and explicit feature differencing. Extensive experiments on multimodal benchmarks demonstrate that the proposed method consistently outperforms state-of-the-art unsupervised multimodal change detection approaches.