2023/12/28 by Mingxiang Cao, Cao, Mingxiang, Weiying Xie +9
Earth and Planetary Sciences · Engineering · #Advanced SAR Imaging Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Synthetic Aperture Radar (SAR) Applications and Techniques #Underwater Acoustics Research
paper · pdf · doi:10.48550/arxiv.2312.16943
openalex publication_date 2023/12/28 · openalex created_date 2023/12/30 · openalex updated_date 2026/07/28
Deep learning has driven significant progress in object detection using Synthetic Aperture Radar (SAR) imagery. Existing methods, while achieving promising results, often struggle to effectively integrate local and global information, particularly direction-aware features. This paper proposes SAR-Net, a novel framework specifically designed for global fusion of direction-aware information in SAR object detection. SAR-Net leverages two key innovations: the Unity Compensation Mechanism (UCM) and the Direction-aware Attention Module (DAM). UCM facilitates the establishment of complementary relationships among features across different scales, enabling efficient global information fusion and transmission. Additionally, DAM, through bidirectional attention polymerization, captures direction-aware information, effectively eliminating background interference. Extensive experiments demonstrate the effectiveness of SAR-Net, achieving state-of-the-art results on aircraft (SAR-AIRcraft-1.0) and ship datasets (SSDD, HRSID), confirming its generalization capability and robustness.