Wavelet thresholding and F-NLM filtering based denoising algorithms applied to high resolution SAR ship detection

Abstract High resolution (HR) SAR ship images have distinctive features of multi-scale, multi-scene and densely arranged distribution of targets, while it is still challenging to have fast and accurate detection. To address the above problem, a SAR ship target detection framework using wavelet thresholding and Fast NLM (F-NLM) joint filtering is proposed. Firstly, wavelet thresholding and F-NLM are used to filter and de-noise the high-resolution SAR images to reduce background clutter noise, while enhancing the detection target detail features and edge information, solving the problem of high false alarm rate in multi-scale, inshore and offshore scenes of ships in SAR images of high resolutions.Then, the YOLOv5 detection network combined with the bi-directional feature fusion module (Bi-FPN) is selected to enable the model to better balance feature information. It can strengthen the aggregation of low and high semantic information and further improve the accuracy of the model. Experimental results show that the SAR ship detection framework has better robustness and target detection accuracy than other deep-learning based algorithms. Compared with SSDD and YOLOv5 network model, the Average Precision (AP) is improved by 1.33% and 2.25% respectively.