南京航空航天大学学报（英文版）2024，Vol.41Issue(6)：725-738,14.DOI:10.16356/j.1005-1120.2024.06.005
面向星载SAR图像的双域联合密集多小舰目标检测算法
Dual-domain Joint Dense Multiple Small Ship Target Detection Algorithm for Spaceborne SAR Images
摘要
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.关键词
合成孔径雷达/小型船舶探测/深度学习/注意力模块/YOLO/双域联合Key words
synthetic aperture radar(SAR)/small ship detection/deep learning/attention module/YOLO/dual-domain joint分类
航空航天引用本文复制引用
贾鹏,董天成,汪韬阳,张过,盛庆红,李俊..面向星载SAR图像的双域联合密集多小舰目标检测算法[J].南京航空航天大学学报（英文版）,2024,41(6):725-738,14.基金项目
This work was supported by the Foundation Strengthening Fund Project(No.2021-JCJQ-JJ-0251) （No.2021-JCJQ-JJ-0251）
in part by the National Natural Science Foundation of China(Nos.42301384 and 42271448). （Nos.42301384 and 42271448）
