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.
Feature Distribution Transfer Learning for Robust Few-Shot ISAR Space Target Recognition
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 6 ; 9129-9142
2024-12-01
3624522 byte
Article (Journal)
Electronic Resource
English
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