LAUSR

Ship detection in synthetic aperture radar (SAR) images has important application value. Sea clutter, complex scenes, a large size change in ships, and the arbitrary directionality of ships make ship detection challenging. With the development of deep learning, many deep learning algorithms have been applied to SAR images. These algorithms need a lot of labeled data for training. It is time-consuming to label SAR data, and the unlabeled data are easy to obtain. It is necessary to use the unlabeled data effectively to improve the performance of the algorithm. In this study, a semisupervised SAR ship detection network, named the semisupervised consistency learning adversarial network (SCLANet), is presented. SCLANet is a two-stage detection network. The local features around the ship can be extracted by the SCLANet, and the features generated from unlabeled data become closer to those generated from labeled data by using adversarial learning. There are two consistency learning modules in SCLANet: noise robustness consistency learning and output encoding consistency learning. Noise robustness consistency learning can increase the robustness of the SCLANet. Maintaining consistency between the noisy results and the original results can train the unlabeled data. In output encoding consistency learning, outputs are mapped to a picture that is fed into an encoder to obtain the intermediate representation embedding. Another embedding is a layer in the main network of the SCLANet. Reducing the error between two embeddings can train the SCLANet with unlabeled data. Two types of consistency learning can be used as pretext tasks for semisupervised learning. Experiments were conducted on two SAR ship datasets. Compared with other algorithms, the SCLANet achieved the highest detection accuracy, indicating that it is more advantageous to use in ship detection.