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.


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    Titel :

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


    Beteiligte:
    Xue, Ruihang (Autor:in) / Bai, Xueru (Autor:in) / Yang, Minjia (Autor:in) / Chen, Bowen (Autor:in) / Zhou, Feng (Autor:in)


    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    3624522 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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