Semantic scattering graph structure alignment for cross-sensor SAR image target detection

Cross-sensor Synthetic Aperture Radar (SAR) target detection suffers from severe performance degradation due to the inconsistency of sensors between training and testing data. Structural characteristics of SAR targets, as important intrinsic properties, play a crucial role in robust detection. However, variations in sensor types often cause inconsistencies in structural details, leading to distribution differences and domain shifts. To address these challenges, we propose a cross-sensor SAR target detection method based on semantic scattering graph structure alignment. First, a semantic scattering graph is constructed from sampled scattering points to characterize the target’s intrinsic structure. Meanwhile, the semantic node associations are enhanced with cross-domain statistical distributions to enrich node prior information and a graph convolutional network to strengthen contextual awareness. Then, a hierarchical structure alignment mechanism is introduced to calibrate structural consistency across domains. This includes the cross-domain perceptual interaction to narrow the semantic distribution differences. Furthermore, the hierarchical alignment is operated at both the local node level and the global structure level, achieving the alignment of target structural consistency. Finally, the experiments conducted on cross-sensor tasks demonstrate that our method significantly outperforms the state-of-the-art methods, achieving improvements of 5%-40% in mAP and F1-score, which highlights the effectiveness of the proposed approach.