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.

Synthetic aperture radar (SAR) target recognition is a fundamental task in SAR image interpretation, which has made tremendous progress with the advancement of artificial intelligence technology. However, SAR imaging is sensitive to the operating conditions of platforms, resulting in large distribution discrepancy for the data collected on different platforms. Moreover, SAR target images are difficult to annotate due to the blurry textures, resulting in insufficient labeled data to train a model. Therefore, subject to the data distribution discrepancy and insufficient labeled data, SAR target recognition becomes a highly challenging task. Transfer adaptive learning (TAL) is a learning paradigm aimed at completing target tasks by transferring knowledge from relevant source domains, which is a promising technique for solving the aforementioned problems in SAR target recognition. However, there is currently no comprehensive survey about the application of transfer adaptation learning in SAR target recognition. To this end, we comprehensively summarized the development of transfer adaptive learning in SAR target recognition, and provided systematic guidance for future research. In our paper, we first summarized the electromagnetic features and visual features of SAR images used for target recognition, which can be potentially used for knowledge transfer. Then, we systematically reviewed the related literature according to the homogeneity of the transfer domains, the modality of the data in the source domain, and the category of the TAL methods. The available datasets that can be used to validate the TAL methods for SAR target recognition were also summarized for the researcher's convenience. We also conducted comparative experiments on these data to demonstrate the performance of TAL methods. Finally, we analyzed the main challenges of the current methods and pointed out several directions worth studying in the future.

How to effectively identify vehicles in SAR (synthetic aperture radar) images is a challenging problem in the SAR image understanding community. Compared with the bounding-box-based localization by object detection networks, object segmentation could provide the complete description of vehicles, which not only detects vehicles’ positions but also obtains their contours. However, vehicle segmentation in SAR images is a hard task due to speckle noise. To address this problem, this letter proposes a focal-pyramid-based network for vehicle segmentation in SAR images, called FVSNet, consisting of a focal pyramid module and a segmentation module. The focal pyramid module is built for extracting the multi-scale semantic feature maps from inputted SAR images, where the channel weights of the feature maps from backbone are automatically adjusted via a channel attention and the ambiguous features derived from speckle noise are eliminated via a designed mask-based cross attention. The segmentation module is designed to enlarge the feature maps outputted from the focal pyramid module with higher-resolution feature maps to obtain vehicle segmentation results. In addition, considering that there exists no open-source manually annotated dataset for handling the SAR vehicle segmentation task, we build and release a SAR vehicle-segmentation dataset based on the public MSTAR dataset. Experimental results on this dataset demonstrate the effectiveness of the proposed network in comparison with eight existing networks.