DRSNet: Rotated-ROI Ship Segmentation for SAR Images Based on Dual-Scale Cross Attention

For ships in synthetic aperture radar (SAR) images, which have various aspect ratios and incline angles, the rotated regions of interest (ROIs) are used in instance segmentation recently as compact segmentation patches. Although the segmentation accuracy of ships has been improved with rotated ROIs, their contour details are unsatisfactory. To refine the contour details, a dual-scale-feature-fusion segmentation network, called DRSNet, is presented in this letter around rotated ROIs. To upgrade the performance of ROI detection, the Gaussian-distribution-based ATSS (Ga-ATSS) is proposed to raise positive samples according to the shape information of ships for training of the bounding-box detection module (DetHead). To enhance the segmentation of contour details, a dual-scale cross attention (DS Cross Attention) is designed to extract accurate detail features from low-scale features and add them into high-scale semantic features in the rotated ROIs. The experimental results reveal that the DRSNet can achieve average precisions (APs) of 67.5% on the SAR ship detection dataset (SSDD) and 68.1% on the instance rotated ship detection dataset (Instance-RSDD), which outperforms the comparison networks, and ablation study shows that the Ga-ATSS can improve the ship detection performance, especially Recall rises by 1.3%.