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ZHOU Zheng, ZHAO Lingjun, HE Qishan, et al. Research progress and prospects of SAR image target detection based on multi-source information cross-domain learning[J]. Journal of Radars, in press. doi: 10.12000/JR25205
Citation: ZHOU Zheng, ZHAO Lingjun, HE Qishan, et al. Research progress and prospects of SAR image target detection based on multi-source information cross-domain learning[J]. Journal of Radars, in press. doi: 10.12000/JR25205
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Research Progress and Prospects of SAR Image Target Detection Based on Multi-source Information Cross-domain Learning

DOI: 10.12000/JR25205 CSTR: 32380.14.JR25205

  • State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, ChangSha 410073, China

Funds:  The National Natural Science Foundation of China (62471475), Hunan Provincial Natural Science Foundation of China (2024JJ4045), Postgraduate Scientific Research Innovation Project of Hunan Province (2022-ZZKY-JJ-10-02), Postgraduate Scientific Research Innovation Project of National University of Defense Technology (XJQY2025014)
More Information
  • Corresponding author: JI Kefeng, jikefeng@nudt.edu.cn
  • Received Date: 2025-10-16
    Available Online: 2025-11-29
    Abstract
  • Deep learning is primarily used for target detection in synthetic aperture radar (SAR) images; however, its performance heavily relies on large-scale labeled datasets. The detection performance of deep learning models degrades when applied to SAR data with varying distributions, hindering their real-world applicability. In addition, manual labeling of SAR data is costly. Hence, cross-domain learning strategies based on multisource information are being explored to address these challenges. These strategies can assist detection models in realizing cross-domain knowledge migration by integrating prior information from optical remote sensing images or heterogeneous SAR images acquired from different sensors. This paper focuses on cross-domain learning technologies within the deep learning framework. In addition, it provides a systematic overview of the latest research progress in this field and analyzes the core issues, advantages, and applicable scenarios of existing technologies from a methodological perspective. It outlines future research directions based on the law of technological evolution, aiming to offer theoretical support and methodological references to enhance the generalizability of target detection in SAR images.

     

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