A Graph-Semantic Guided Transformer Network for Remote Sensing Image Change Detection

In remote sensing image (RSI) change detection (CD), the binary CD is crucial for identifying change objects but lacks semantic information, leading to issues with intraclass similarity and differentiation. This letter proposes a graph-semantic guided Transformer network (GGTNet), by leveraging semantic information and graph convolution networks. Our framework integrates CNNs and Transformer layers in a multilayered parallel and serial manner and introduces a novel graph semantic module (GSM) to guide detailed feature extraction through graph semantic features. In this module, each channel of the feature map is treated as a feature node, and their relationships are represented by an adjacency matrix, with multihop neighbor aggregation enhancing the model’s ability to capture long-range dependencies, resulting in richer graph semantic features. These features are then utilized in the subsequent semantic fusion Transformer module (SFTM) to guide the backbone features extracted by the network. Experimental results demonstrate that our method is competitive compared to several state-of-the-art CD methods.