BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap

Recently, deep learning methods have been widely adopted for ship detection in synthetic aperture radar (SAR) images. However, many of the existing methods miss adjacent ship instances when detecting densely arranged ship targets in inshore scenes. Besides, they suffer from the lack of precision in the instance indication information and the confusion of multiple instances by a single mask head. In this article, we propose a novel center point prediction algorithm, which detects the center points by finding a long-distance variation relationship between two points. The whole prediction process is anchor-free and does not require additional bounding box (BBox) predictions for nonmaximum suppression (NMS). Therefore, our algorithm is BBox-free and NMS-free, solving the problem of low recall rates when conducting NMS for densely arranged targets. Furthermore, to tackle the deficiency of position indication information in localization tasks, we introduce a feature fusion module with feature decoupling (FD). This module uses the classification branch to provide guidance information for the localization branch while suppressing the influence of the gradient flow mixing, effectively improving the algorithm's segmentation performance of ship contours. Finally, through principal component analysis (PCA) of the Gaussian distribution covariance matrix, we propose a loss function based on the distance between centroids and the difference of angle, called centroid and angle constraint (CAC). CAC guides the network in learning the criterion that a single dynamic mask head is only valid for a single instance. Experiments conducted on polygon segmentation SAR ship detection dataset (PSeg-SSDD) and high-resolution SAR images dataset (HRSID) demonstrate the effectiveness and robustness of our method.