Ship Instance Segmentation Based on Rotated Bounding Boxes for SAR Images

Abstract

Ship instance segmentation in synthetic aperture radar (SAR) images is a hard and challenging task, which not only locates ships but also obtains their shapes with pixel-level masks. However, in ocean SAR images, because of the consistent reflective intensities of ships, the appearances of different ships are similar, thus making it far too difficult to distinguish ships when they are in densely packed groups. Especially when ships have incline directions and large aspect ratios, the horizontal bounding boxes (HB-Boxes) used by all the instance-segmentation networks that we know so far inevitably contain redundant backgrounds, docks, and even other ships, which mislead the following segmentation. To solve this problem, a novel ship instance-segmentation network, called SRNet, is proposed with rotated bounding boxes (RB-Boxes), which are taken as the foundation of segmentation. Along the directions of ships, the RB-Boxes can surround the ships tightly, but a minor deviation will corrupt the integrity of the ships’ masks. To improve the performance of the RB-Boxes, a dual feature alignment module (DAM) was designed to obtain the representative features with the direction and shape information of ships. On account of the difference between the classification task and regression task, two different sampling location calculation strategies were used in two convolutional kernels of the DAM, making these locations distributed dynamically on the ships’ bodies and along the ships’ boundaries. Moreover, to improve the effectiveness of training, a new adaptive Intersection-over-Union threshold (AIoU) was proposed based on the aspect-ratio information of ships to raise positive samples. To obtain the masks in the RB-Boxes, a new Mask-segmentation Head (MaskHead) with the twice sampling processes was explored. In experiments to evaluate the RB-Boxes, the accuracy of the RB-Boxes output from the Detection Head (DetHead) of SRNet outperformed eight rotated object-detection networks. In experiments to evaluate the final segmentation masks, compared with several classic and state-of-the-art instance-segmentation networks, our proposed SRNet achieved more accurate ship instance masks in SAR images. The ablation studies demonstrated the effectiveness of the DAM in the SRNet and the AIoU for our network training.