中国舰船研究2024，Vol.19Issue(6)：275-283,9.DOI:10.19693/j.issn.1673-3185.03477
联合小波阈值和F-NLM去噪的高分辨率SAR舰船检测方法
Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection
摘要
Abstract
[Objective]Aiming at the significant features of high-resolution synthetic aperture radar(SAR)ship targets with multiple scenes,multi-scale and dense arrangements,and the problem of the blurring of tar-get edge details due to coherent noise in the imaging process,a high-resolution SAR ship detection method is proposed with joint wavelet thresholding and fast non-local mean(F-NLM)de-noising.[Methods]First,wavelet thresholding and F-NLM de-noising modules are utilized to preprocess the SAR image and reduce the sea clutter noise,enhance the detailed features and edge information of the detection target,and make the ex-tracted features more discriminative.Next,a YOLOv7 detection algorithm combined with a bi-directional fea-ture pyramid network(Bi-FPN)is selected to effectively aggregate the multi-scale features and further im-prove the model's accuracy.[Results]The experimental results show that the average precision of ship de-tection using the de-noised dataset D-SSDD can reach 98.69%and the false alarm rate is reduced to 2.37%.[Conclusions]It is clear that the proposed high-resolution SAR ship detection method not only homogen-izes the background clutter to improve the image quality,but also improves the interactivity of multi-scale fea-ture information to ensure precise and accurate target detection.关键词
雷达目标识别/图像处理/SAR舰船检测/小波变换/小波阈值/快速非局部均值滤波/双向特征金字塔网络(Bi-FPN)/YOLOv7Key words
radar target recognition/image processing/SAR ship detection/wavelet transforms/wavelet threshold/fast non-local mean(F-NLM)/bi-directional feature pyramid network(Bi-FPN)/YOLOv7分类
交通工程引用本文复制引用
童亮,刘丹,彭中波,邹涵,王露萌,张春玉..联合小波阈值和F-NLM去噪的高分辨率SAR舰船检测方法[J].中国舰船研究,2024,19(6):275-283,9.基金项目
重庆市科学技术委员会资助项目(2022TIAD-GPX0018) （2022TIAD-GPX0018）
重庆交通大学研究生科研创新资助项目(2023S0076) （2023S0076）
