A Category–Pose Jointly Guided ISAR Image Key Part Recognition Network for Space Targets

It is a crucial interpretation task in space target perception to identify key parts of space targets through the inverse synthetic aperture radar (ISAR) imaging. Due to the significant variations in the categories and poses of space targets, conventional methods that directly predict identification results exhibit limited accuracy. Hence, we make the first attempt to propose a key part recognition network based on ISAR images, which incorporates the knowledge of space target categories and poses. Specifically, we propose a fine-grained category training paradigm that defines the same functional parts of different space targets as distinct categories. Correspondingly, additional classification heads are employed to predict category and pose, and the predictions are then integrated with ISAR image semantic features through a designed category–pose guidance module to achieve high-precision recognition guided by category and pose knowledge. Qualitative and quantitative evaluations on two types of simulated targets and one type of measured target demonstrate that the proposed method reduces the complexity of the key part recognition task and significantly improves recognition accuracy compared to the existing mainstream methods.