Few-Shot Classification for ISAR Images of Space Targets by Complex-Valued Patch Graph Transformer

Inverse synthetic aperture radar (ISAR) is an important detection approach for the classification of space targets, but the few-shot circumstances often occur due to the limitation of the imaging conditions and the large attitude changes of the maneuvering targets. At present, data augmentation and metric learning are mainly used to solve the few-shot classification problems. However, these methods are not effective when facing the space targets with large attitude changes because they can only extract the global features and it is difficult for them to obtain an effective representation of the internal features of the image. This article proposed a complex-valued graph classification framework, which can avoid the loss of the phase information of ISAR images. Besides, a module that can extract the rich spatial relationships between image regions and effective representations is constructed. It uses the graph information reasoning method and the transformer structure to extract the contextual features between image regions and overcomes the classification problems caused by large attitude changes of space targets. Furthermore, a contrast learning method is introduced to reduce the impact on classification caused by the defocusing on the images, attitude changes of targets, and imaging parameters of radars. Finally, experimental results by simulation data under different imaging parameters and laboratory-measured data demonstrate that the proposed method can get more accurate results and exhibits more robustness than other few-shot learning methods for targets with large attitude changes.