AEROSPACE SHANGHAI, Volume. 41, Issue 3, 121(2024)

A Multi-domain Feature-guided Method for Unsupervised Ship Detection in SAR Images

Liang CHEN*... Jianhao LI, Cheng HE and Hao SHI |Show fewer author(s)
Author Affiliations
  • School of Information and Electronics,Beijing Institute of Technology,Beijing100081,China
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    How to improve the ship detection performance with limited annotation samples in a synthetic aperture radar (SAR) image has always been a hot spot in SAR image processing.In this paper,a multi-domain feature-guided unsupervised domain adaptation method is proposed.The knowledge is transferred from the annotated source domain (optical images) to the unannotated target domain (SAR images),and thus the dependency on the labeled SAR images is reduced.At the same time,the frequency domain transfer module,attention area-enhanced (AAE) module,and adaptive weighted module are designed to narrow the domain gap between the optical and SAR image domains,improve the efficiency of feature alignment between the source and target domains,and enhance the capability of feature transfer under challenging samples.Extensive experiments are carried out on public published datasets.The results show that the proposed modules are 10% better than the baseline,and the overall network outperforms other state-of-the-art (SOTA) methods.

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    Liang CHEN, Jianhao LI, Cheng HE, Hao SHI. A Multi-domain Feature-guided Method for Unsupervised Ship Detection in SAR Images[J]. AEROSPACE SHANGHAI, 2024, 41(3): 121

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    Paper Information

    Category: Innovation and Exploration

    Received: May. 6, 2024

    Accepted: --

    Published Online: Sep. 3, 2024

    The Author Email:

    DOI:10.19328/j.cnki.2096-8655.2024.03.013

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