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 target 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.

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 extracted features more discriminative. Next, a YOLOv7 detection algorithm combined with a bi-directional feature pyramid network (Bi-FPN) is selected to effectively aggregate the multi-scale features and further improve the model's accuracy.

The experimental results show that the average precision of ship detection using the de-noised dataset D-SSDD can reach 98.69% and the false alarm rate is reduced to 2.37%.

It is clear that the proposed high-resolution SAR ship detection method not only homogenizes the background clutter to improve the image quality, but also improves the interactivity of multi-scale feature information to ensure precise and accurate target detection.