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.

中文翻译：

---

利用复值块图变换器对空间目标 ISAR 图像进行少样本分类


逆合成孔径雷达(ISAR)是空间目标分类的重要探测手段，但由于成像条件的限制和机动目标姿态变化较大，常常出现少炮情况。目前，数据增强和度量学习主要用于解决少样本分类问题。然而，这些方法在面对姿态变化较大的空间目标时效果不佳，因为它们只能提取全局特征，难以获得图像内部特征的有效表示。本文提出了一种复值图分类框架，可以避免ISAR图像相位信息的丢失。此外，还构建了一个可以提取图像区域和有效表示之间丰富的空间关系的模块。它利用图信息推理方法和Transformer结构来提取图像区域之间的上下文特征，克服了空间目标姿态变化较大带来的分类问题。此外，引入对比学习方法，减少图像离焦、目标姿态变化、雷达成像参数等对分类的影响。最后，不同成像参数下的仿真数据和实验室测量数据的实验结果表明，对于姿态变化较大的目标，该方法能够得到更准确的结果，并且比其他少样本学习方法表现出更强的鲁棒性。