SAR ship localization method with denoising and feature refinement

Abstract

Synthetic Aperture Radar (SAR) ship detection is greatly important to marine transportation monitoring and fishery resource management. To improve the detection accuracy of small ships, an SAR ship localization method with Denoising and Feature Refinement (DFR) is proposed in this paper. It consists of three parts. The first part is the denoising module, which uses non-local mean to suppress the speckle noise of the SAR image. The second part is Hierarchical Feature Fusion (HFF) module. It can integrate more low-level features by adding skip connections. This prevents the low-level spatial position information of the fused features from being diluted by high-level semantic information, therefore it is beneficial to the detection of small ships. The third part is a center-based ship predictor with Feature Refinement (FR). The FR module is proposed to refine the features and reduce the background interference, which is conducive to locate ships more accurately. Extensive experiments are conducted. The experimental results show that after adding the denoising and FR modules, the value of AP0.5 is increased by 1.7% and 2.3%, respectively, which proves the effectiveness of these two modules. In inshore and offshore scenarios, the AP0.5 values of DFR are 0.884 and 0.966, respectively, achieving the best results. The proposed method can also be generalized to mark lesion locations in medical images and detect offshore oil production platforms.