Unsupervised aircraft detection in SAR images with image-level domain adaption from optical images

Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in both civil and military fields. However, due to the special imaging mechanism of SAR images, the aircraft annotating process is easily affected by interferences and noises in the background, leading to a high labeling cost. As most object detection networks are trained in a supervised manner, a serious problem of applying them to SAR aircraft detection tasks is the insufficient training data. To address this problem, we propose an unsupervised domain adaption method for the training of SAR aircraft detectors. First, we propose to transfer knowledge from optical aerial images in which aircraft annotations are easier to obtain. By adopting an image-level domain adaption, the target information in optical images can be utilized for the training of SAR aircraft detectors. Then, CycleGAN is adopted to overcome the discrepancy between optical and SAR domains by image-style translation. To evaluate the effectiveness of the proposed method, we build up an optical-to-SAR aircraft detection dataset (O2SADD) based on existing public datasets. Experiments on O2SADD indicate that the proposed method can significantly improve the performance of SAR aircraft detectors without manually annotating aircraft in SAR images.