A SEMI-SUPERVISED SAR SHIP DETECTION FRAMEWORK VIA LABEL PROPAGATION AND CONSISTENT AUGMENTATION

Deep neural networks have been widely applied and researched in synthetic aperture radar (SAR) object detection and achieved a great success. However, deep supervised networks heavily rely on a large amount of labeled data, while the annotation is difficult and time-consuming to obtain. But the unlabeled data are comparably easier to get. Considering that, we introduce a semi-supervised learning framework for SAR object detection, which is built via label propagation and consistent augmentation. The experiments on a SAR ship dataset prove that the introduced semi-supervised training framework can achieve higher detection performance with utilizing the unlabeled data compared with the corresponding supervised object detection network.