Precise Instance Segmentation Network for High-Resolution SAR Images

Abstract

With the development of synthetic aperture radar (SAR) system technology and the wide application of deep learning technology, ship detection on SAR images has rapidly developed. Benefit from the strong generalization ability and end-to-end training capabilities, convolution neural network (CNN) based ship detection methods have the proprietary advantage in high-performance SAR ship detection. However, relevant SAR ship detection methods adopt rectangular bounding boxes to locate the ships which are unable to extract the contours feature of the ships. To solve this problem, we proposed a precise instance segmentation network for high-resolution SAR images. The method combines the bottom-up path augmentation module, global context module, and soft non-maximum suppression to improve Mask R-CNN for segmenting high-resolution SAR ships in pixel-wise. The network is trained and tested on the high-resolution SAR images dataset (HRSID), and the ablation experiments are conducted with Microsoft Common Objects in Context (MS COCO) evaluation metrics to verify the effects of each module. Quantitatively, the experimental results show that the method exceeds vanilla Mask R-CNN 2% AP in instance segmentation of high-resolution SAR images. Meanwhile, the visualized instance segmentation results indicate that our method fits the practical application, and it possesses the ability to extract the contour of the ship, which is more conducive to the instance segmentation of SAR images.