Feature Distribution Transfer Learning for Robust Few-Shot ISAR Space Target Recognition

Due to the strict observation conditions and special target attributes, inverse synthetic aperture radar (ISAR) may suffer with insufficient number of images for certain space targets, which leads to a considerable decline in the recognition performance. In this article, we propose a robust space target recognition method for sequence ISAR images based on feature distribution transfer learning. To obtain deformation robust sequential features, a sequence homography network is first proposed and trained by semi-supervised learning. Then the extracted embedding features are aligned and transferred to the class label domain by optimal transport mapping. Target recognition experiments on a few-shot satellite data set illustrate that the proposed method has higher average accuracy and better robustness for scaled, rotated, and combined image deformation.