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LIU Changhao, WANG Yan, ZHAO Siyu, et al. Review of deep learning for SAR 3D imaging[J]. Journal of Radars, 2026, 15(1): 1–25. doi: 10.12000/JR25163
Citation: LIU Changhao, WANG Yan, ZHAO Siyu, et al. Review of deep learning for SAR 3D imaging[J]. Journal of Radars, 2026, 15(1): 1–25. doi: 10.12000/JR25163
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Review of Deep Learning for SAR 3D Imaging

DOI: 10.12000/JR25163 CSTR: 32380.14.JR25163
  • 1.

    Institute of Radar Technology, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    Yangtze Delta Region Academy of Beijing Institute of Technology (Jiaxing), Jiaxing 314000, China

  • 3.

    Beijing Institute of Tracking and Telecommunications Technology, Beijing, 100094, China

Funds:  The National Natural Science Foundation of China (62271053, 62331007, 62422101)
More Information
  • Corresponding author: WANG Yan, yan_wang@bit.edu.cn
  • Received Date: 2024-03-01
  • Rev Recd Date: 2026-01-08
    • Available Online: 2026-01-15
    Abstract
  • With the increasing demands on imaging accuracy, efficiency, and robustness in modern three-Dimensional (3D) Synthetic Aperture Radar (SAR) imaging systems, the performance of traditional 3D imaging methods, such as matched filtering and compressed sensing, has become limited in these aspects. In recent years, the rapid development of Deep Learning (DL) technology has provided new theoretical solutions for SAR 3D imaging by enabling the integration of neural networks with physical radar imaging models, leading to the emergence of a learning-based imaging paradigm that combines data-driven and model-driven approaches. This paper systematically reviews recent research progress in DL-based SAR 3D imaging. Focusing on two core issues, namely super-resolution imaging and enhanced imaging, this paper discusses current research advances and hotspots in SAR 3D imaging. These include super-resolution 3D imaging methods based on feedforward neural networks and deep unfolding networks, as well as 3D enhancement techniques such as multichannel data preprocessing and point cloud post-processing. This paper also summarizes publicly available datasets for SAR 3D imaging. In addition, this paper explores current research challenges in DL SAR 3D imaging, including high-generalization and high-precision DL SAR super-resolution 3D imaging technology, DL SAR elevation dimension disambiguation technology, integrated study of DL SAR 3D imaging and image enhancement, and the construction of DL SAR 3D imaging datasets. This paper provides an outlook on future development trends, aiming to offer research references and technical guidance for scholars in related fields.

     

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