基于窗口注意力机制的可见光-SAR跨模态目标匹配方法OA

This study addresses the issues of low efficiency and accuracy in target matching within remote sensing ship tracking scenarios,which are attributed to significant modal differences between optical images and synthetic aperture radar(SAR)images,as well as the inadequacy of cross-modal matching technology.In response,a novel optical-SAR cross-modal target matching method employing a window attention mechanism is proposed.The method designed a cross-modal dual-branch embedding module to process the two image types separately and extracted modality-agnostic features via a hierarchical window attention mechanism.It fused the modal information embedding and ship-size embedding to supplement the semantic and physical attribute information of ships and to enhance the learning of cross-modal-aligned features.Experimental results show that the proposed method achieves an overall mean Average Precision(mAP)of 46.0%,Top-1(R1)matching accuracy of 60.8%,Top-5(R5)matching accuracy of 74.4%,and Top-10(R10)matching accuracy of 79.5%on the HOSS dataset.Compared with the state-of-the-art TransOSS model,R5 and R10 achieve improvements of 3.4%and 1.1%respectively.The key matching indicators in both the optical-to-SAR and SAR-to-optical directions are superior to those of the current optimal model.The research indicates that the proposed method outperforms the SOTA(state-of-the-art)model and provides technical support for continuous ship tracking across scenarios such as maritime search and rescue and shipping supervision.