DSENet: A Detail and Semantic Enhanced Network for Video SAR Moving Target Shadow Detection

In video synthetic aperture radar (Video SAR), target motion causes defocusing, making it impossible to determine the target’s real-time position using reflected echoes. However, the shadows formed by the target occluding ground reflections can accurately characterize the target’s real-time position. To address challenges such as varying shadow scales, low contrast with the moving background, and susceptibility to clutter interference, this paper proposes a shadow detection network called DSENet to enhance the detail and semantic features of shadows. First, to enhance shadow features and reduce sampling loss during backbone network feature extraction, we design a detailed information enhancement (DIE) module to achieve lossless downsampling and effectively preserve the detailed features of the shadowed target. Second, we propose a semantic spatial feature aggregation (SSFA) module to enhance global semantic space feature extraction, improve the contextual feature representation of the target’s shadow region, and provide robust semantic space prior information for the model. Finally, we designed a detailed semantic fusion (DSF) module to improve the neck network’s ability to fuse shadow details and semantic features in video SAR images, further enhancing the model’s localization performance for target shadow features and achieving accurate localization of moving targets in video SAR. Comparative and ablation experiments validate the effectiveness and superiority of the proposed method. Experimental results on the Sandia National Laboratories (SNL) public dataset demonstrate that DSENet is efficient and performs excellently, achieving a P of 92.4% and an F1 score of 83.1%.