Ship Segmentation via Encoder-Decoder Network With Global Attention in High-Resolution SAR Images

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder-decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder-decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.