联合小波阈值和F-NLM去噪的高分辨率SAR舰船检测方法OA北大核心CSTPCD

[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.