BGSINet-CD: Bitemporal Graph Semantic Interaction Network for Remote-Sensing Image Change Detection

Significant progress has been made in modern remote-sensing (RS) image change detection (CD) by leveraging the powerful feature learning capabilities of convolutional neural networks (CNNs) and transformers. However, current popular CD techniques primarily focus on extracting deep semantic features and pixel-level interactions while overlooking the potential benefits of cluster-level semantic interaction in bitemporal images. In this letter, we propose a novel approach called the bitemporal graph semantic interaction network for RS image CD (BGSINet-CD). Specifically, the land cover types in bitemporal images are clustered by employing soft clustering for each pixel, and then each cluster is separately projected to a vertex in graph space. Additionally, we introduce a graph semantic interaction module (GSIM) that enhances the interactions between bitemporal features at the semantic level. GSIM effectively improves the information coupling between bitemporal features, thereby suppressing task-irrelevant information. In comparison to other competing methods, our approach demonstrates a significant improvement in
$F1$
scores, achieving 88.25% and 91.02% on the google data set (GZ)-CD and WHU building change detection dataset (WHU), respectively. Furthermore, our method employs a reduced number of network parameters and exhibits lower complexity, striking a superior balance between accuracy and computational efficiency.