ISAR Image Segmentation for Space Target Based on Contrastive Learning and NL-Unet

Peng Kou, Xiangfeng Qiu, Yongxiang Liu, Dongjie Zhao, Weijie Li, Shuanghui Zhang

Abstract: The inverse synthetic aperture radar (ISAR) images are often afflicted by boundary-blurring, discontinuity, sidelobe effects of strong scattering points, a large dynamic range of gray values, and azimuth defocus, which pose significant challenges to image segmentation. This letter proposes a novel semantic segmentation method for ISAR images of space targets. The method is based on contrastive learning (CL) and nonlocal Unet (NL-Unet). First, the method roughly segments the target contour using binary semantic tags to remove sidelobe interference and image noise. Then, the nonlocal (NL) self-attentive mechanism with a global perceptual field is used to exploit the structural symmetry of the ISAR image. Finally, to improve the segmentation ability of target small parts, the method adopts a training method based on CL to overcome the relatively weak problem of the supervised learning model. The proposed method outperforms existing methods on the simulated ISAR dataset. Moreover, it is practical and can be directly generalized to the real-measured dataset without retraining.