Graph Contrastive Learning and Multigraph Attention Fusion Network for Hyperspectral Image Change Detection

In recent years, graph neural networks (GNNs) have been increasingly applied to the hyperspectral image change detection (HSI-CD). However, existing methods propagate features by weighting nodes and edges, which may weaken the original node characteristics and spectral differences. Moreover, current methods typically treat bitemporal features in isolation and lack intertemporal semantic modeling. To address the above issues, this article proposes a graph contrastive learning and multigraph attention fusion network (GCLMA), which leverages the contrastive learning to extract discriminative features and enhances semantic relevance through graph attention-based interactions across multiple graphs. The proposed GCLMA method mainly consists of four modules, i.e., spatial–spectral graph construction (SSGC), contrastive feature learning ResGCN (CFLR), differential feature enhancement (DFE), and multigraph interactive fusion (MGIF). The SSGC module is first designed to construct a well-defined graph structure by adaptively integrating the spatial proximity and spectral similarity. Then, the CFLR module utilizes the contrastive learning to extract discriminative feature representations that are sensitive to subtle changes. Next, the DFE module amplifies significant spectral variations to emphasize critical change regions. Finally, the MGIF module employs the graph attention mechanism to interactively fuse enhanced difference graph features with bitemporal graph features, thereby facilitating the semantic modeling. Experimental results on three HSI-CD datasets show that the proposed GCLMA outperforms most existing state-of-the-art methods. The source code of the proposed method will be released at: https://github.com/YYYYYJJJJJJ/GCLMA