Polarimetric ISAR Space Target Structure Recognition Based on Embedded Scattering Mechanism and Semi-Supervised Representation Learning

Identifying satellite components in polarimetric inverse synthetic aperture radar (ISAR) images is beneficial for monitoring their operation and health status. Most target recognition methods rely on network structures designed for optical images and fail to consider the inherent polarimetric scattering characteristics. Furthermore, the aliasing of scattering mechanisms caused by the complex structure of man-made targets, along with the scattering diversity resulting from observation perspectives, poses challenges to target polarimetric interpretation. To address these challenges, this study proposes a structure recognition framework embedded within scattering mechanism to achieve pixel-level to component-level structure (CS) recognition. First, through semi-supervised representation learning, the 3-D polarimetric correlation pattern (3-D PCP) of typical polarimetric scattering structures (PSSs) is used as expert knowledge to guide a deep-learning network, enabling pixel-level scattering mechanism separation. On this basis, a relation module is employed to explore the relationships between different pixels’ scattering mechanisms to accomplish component-level recognition. Finally, polarimetric ISAR satellite images and component annotation datasets are constructed. Pixel-level and component-level comparisons verify the advantages of the proposed method.