Dual-domain Joint Dense Multiple Small Ship Target Detection Algorithm for Spaceborne SAR Images
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Abstract:
Ship detection via spaceborne synthetic aperture radar (SAR) has become a research hotspot. However, existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accurate results because of the obvious coherent speckle noise at sea and strong reflection interference from near-shore objects. To resolve the above problems, this study proposes a dual-domain joint dense multiple small ship target detection method for spaceborne SAR image that simultaneously detects objects in the image and frequency domains. This method uses an attention mechanism module and algorithm structure adjustments to improve the small ship target feature mining ability. In the frequency-based image generation, extreme signal strength values are detected in the azimuth and range directions, with the results of the two complementing each other to realize dual-domain joint small ship target detection. The comprehensive qualitative and quantitative evaluation results show that the proposed method can attain a final precision rate of 92.25% and achieve accurate results for SAR ship detection in open-sea, coastal, and port area ships. The test results for the self-built SAR small-ship dataset demonstrate the effectiveness and universality of the method.
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This work was supported by the Foundation Strengthening Fund Project (No.2021-JCJQ-JJ-0251); in part by the National Natural Science Foundation of China (Nos.42301384 and 42271448).
JIA Peng, DONG Tiancheng, WANG Taoyang, ZHANG Guo, SHENG Qinghong, LI Jun. Dual-domain Joint Dense Multiple Small Ship Target Detection Algorithm for Spaceborne SAR Images[J]. Transactions of Nanjing University of Aeronautics & Astronautics,2024,(6):725-738