A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This article provides a sidelobe-aware small ship detection network for SAR imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection dataset (LS-SSDD), the SAR Ship Detection dataset (SSDD), and the AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

Automated summary

This article proposes a sidelobe-aware small ship detection network for SAR imagery. Dual-pooling is utilized to build a feature extraction module, which reduces the influence of sidelobes and blurred outlines and enhances ship body information. The network structure is enriched by concatenating different feature maps and a novel loss function based on dual Euclidean distances is introduced to accurately describe overlapping box situations.