深度学习SAR三维成像研究综述

刘常浩; 王岩; 赵思雨; 高安琪

doi:10.12000/JR25163

深度学习SAR三维成像研究综述

DOI: 10.12000/JR25163 CSTR: 32380.14.JR25163

刘常浩¹,
王岩^{1, 2, ,},
赵思雨²,
高安琪³

1.
北京理工大学雷达技术研究院北京 100081
2.
北京理工大学长三角研究院(嘉兴) 嘉兴 314000
3.
北京跟踪与通信技术研究所北京 100094

基金项目: 国家自然科学基金 (62271053, 62331007, 62422101)

详细信息

作者简介:
刘常浩，博士生，主要研究方向为雷达信号处理、深度学习雷达三维成像

王　岩，博士，教授，博士生导师，主要研究方向为新体制雷达探测、成像与智能处理技术

赵思雨，硕士生，主要研究方向为SAR三维成像

高安琪，博士，助理研究员，主要研究方向为SAR成像、雷达系统仿真与总体设计

通讯作者:
王岩 yan_wang@bit.edu.cn

责任主编：焦泽坤 Corresponding Editor: JIAO Zekun

中图分类号: TN95
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出版历程
- 收稿日期: 2025-09-01
- 修回日期: 2026-01-08
- 网络出版日期: 2026-01-30
- 刊出日期: 2026-02-28

Review of Deep Learning for SAR 3D Imaging

LIU Changhao¹,
WANG Yan^{1, 2
, ,},
ZHAO Siyu²,
GAO Anqi³

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

摘要

摘要: 随着现代合成孔径雷达(SAR)三维成像系统对成像精度、效率及稳健性要求的不断提高，传统匹配滤波与压缩感知等方法三维成像性能受限。近年来，深度学习技术的迅猛发展为SAR三维成像提供了新的解决路径，通过将神经网络与雷达成像物理模型深度融合，形成了数据驱动与模型驱动协同的学习成像新范式。该文系统综述了深度学习在SAR三维成像中的研究进展，重点围绕超分辨成像与增强成像两大核心问题，基于前馈神经网络和深度展开网络的超分辨三维成像方法、多通道数据预处理和点云后处理三维增强成像方法论述了目前SAR三维成像方向的研究进展和研究热点，并综述了目前行业已公开发布的SAR三维成像数据集。此外，该文还探讨了当前深度学习SAR三维成像在高泛化高精度深度学习SAR超分辨三维成像技术研究、深度学习SAR高度维解模糊技术研究、深度学习SAR三维成像与图像增强一体化研究、深度学习SAR三维成像数据集构建等方面存在的研究挑战，并对未来发展趋势提出展望，旨在为相关领域学者提供研究参考和技术引导。
- 合成孔径雷达 /
- 三维成像 /
- 深度学习 /
- 超分辨成像 /
- 增强成像
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.
- Synthetic Aperture Radar (SAR) /
- 3D imaging /
- Deep Learning (DL) /
- Super-resolution imaging /
- Enhanced imaging

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图 1 SAR三维成像几何示意图

Figure 1. SAR 3D imaging geometry schematic diagram

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图 2 ISTA与LISTA算法架构对比

Figure 2. Comparison of ISTA and LISTA algorithm architectures

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图 3 文献[27]中提出的基于CS-DNN架构的深度学习超分辨SAR三维成像方法原理图

Figure 3. A schematic diagram of the deep learning-based super-resolution SAR 3D imaging method utilizing the CS-DNN architecture in Ref. [27]

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图 4 文献[27]中基于中国科学院IECAS-Data数据进行的SAR三维成像算法验证结果

Figure 4. SAR 3D imaging algorithm verification results conducted using the IECAS-Data from the Chinese Academy of Sciences, as presented in Ref. [27]

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图 5 文献[43]多航过无人机大合成孔径角SAR三维成像方法原理图

Figure 5. A schematic diagram of the multi-pass UAV large synthetic aperture angle SAR 3D imaging method proposed in Ref. [43]

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图 6 文献[43]中利用多航过无人机大合成孔径角SAR三维成像试验进行的算法对比结果

Figure 6. A comparative analysis of algorithm performance based on multi-pass UAV SAR 3D imaging experiments with large synthetic apertures, as presented in Ref. [43]

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图 7 文献[52]中采用γ-Net获取的TanDEM-X数据的SAR三维成像结果

Figure 7. SAR 3D imaging results of TanDEM-X data acquired using the γ-Net in Ref. [52]

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图 8 文献[58]中采用GITomo-Net获取的北京TerraSAR-X三维成像结果

Figure 8. SAR 3D imaging results of Beijing TerraSAR-X data acquired using the GITomo-Net in Ref. [58]

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图 9 文献[62]中采用提出的RMIST-Net获得的近场毫米波SAR三维成像结果

Figure 9. The 3D imaging results of near-field millimeter-wave SAR obtained by the proposed RMIST-Net in Ref. [62]

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图 10 文献[73]针对多通道SAR数据去噪问题提出的基于深度学习网络的滤波处理架构

Figure 10. The deep learning-based filtering architecture proposed by the in Ref. [73] for multi-channel SAR data denoising

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图 11 文献[74]采用不同滤波方法进行多通道数据滤波预处理后的SAR三维成像结果对比

Figure 11. Comparison of SAR 3D imaging results after preprocessing multi-channel data with different filtering methods in Ref. [74]

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图 12 文献[32]中利用SARMV3D-Imaging-1.0数据集通过不同成像方法和点云滤波方法获取的SAR三维点云图像

Figure 12. SAR 3D point cloud images obtained by different imaging and point cloud filtering methods on the SARMV3D-Imaging-1.0 dataset, as presented in Ref. [32]

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表 1 图6中多航过无人机大合成孔径角SAR三维成像试验的性能指标对比

Table 1. Performance metrics comparison for the multi-pass UAV large synthetic aperture angle SAR 3D imaging experiment by in Fig. 6

方法	SSIM	CD (m)	时间(s)
BP	0.84	1.12	2.69
CS-LSA	0.83	0.88	6998.75
EASTER	0.92	0.80	11.02

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表 2 图8中北京TerraSAR-X三维成像结果的精度对比

Table 2. Accuracy comparison of the Beijing TerraSAR-X 3D imaging results in Fig. 8

方法	AG/IE		时间(s)
方法	区域 A	区域 B	时间(s)
SL1MMER	6.43/2.85	3.39/1.72	13928.25
MAda-Net (无先验构型)	×	×	236.43
MAda-Net (有先验构型)	7.48/2.76	4.06/1.69	236.43
GITomo-Net	7.11/2.85	3.90/1.73	269.88

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表 3 深度学习远场SAR超分辨三维成像方法泛化性对比

Table 3. Comparative analysis of generalization in SAR far-field 3D imaging using deep unfolding networks

网络结构	成像模型	方法	特点	泛化性
前馈神经网络	单标签分类模型	TSNN^[42]	高度维单假设体散射	差
	多标签分类模型	CS-DNN架构^[27]	利用CS空间滤波后进行多标签分类	中
	多标签分类模型	MLC-Net^[44]	网络训练需要观测先验	中
	逐网格点二分类模型	EASTER^[43]	针对大合成孔径角，训练无须观测先验	好
深度展开结构	固定构型的稀疏成像模型	AMP-Net^[36]	基于AMP深度展开	中
		CV-LISTA^[35], γ-Net^[52], ATASI-Net^[54], AETomo-Net^[53], Ada-CLISTAT^[55]	基于ISTA深度展开
		Gated RNN^[56]	循环神经网络架构，考虑历史信息
		ADMM-Net^[37,38]	基于ADMM深度展开
	考虑空变构型的稀疏成像模型	MAda-Net^[57]	将观测特征与网络参数结合	好
	考虑空变构型的稀疏成像模型	GITomo-Net^[58]	网络训练不依赖构型先验	好

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表 4 图9中近场毫米波SAR三维成像试验成像性能比较

Table 4. Imaging performance comparison of the near-field millimeter-wave SAR 3D imaging experiment conducted in Fig. 9

目标	MF			RMA			ISTA			RMIST-Net
目标	ENT	对比度	时间(s)	ENT	对比度	时间(s)	ENT	对比度	时间(s)	ENT	对比度	时间(s)
手枪模型	0.27	8.27	0.06	0.67	9.08	0.11	0.10	12.06	2.33	0.07	14.19	0.07
书架模型	0.66	3.70	0.06	1.19	3.72	0.11	0.50	3.85	2.34	0.35	4.03	0.06
注：加粗数值表示最佳指标值。

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表 5 对图11中的不同滤波方法获取的三维SAR成像结果的性能指标对比

Table 5. The comparison of the 3D SAR imaging results obtained using different filtering methods in Fig. 11

方法	MAE_b1	MAE_b2	MAE_b3	STD_b1	STD_b2	STD_b3
未滤波	1.4485	1.5230	2.9150	1.9082	2.0038	1.5016
非局部滤波	1.1293	1.3801	1.3675	1.4727	1.7815	1.9897
MS-IPDM	1.1133	1.4453	1.2105	1.1689	1.5850	1.3184
注：加粗数值表示最佳指标值。

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表 6 文献[32]中对多种不同方法的SAR三维成像性能的对比

Table 6. Ref. [32] presents a comparison of the SAR 3D imaging performance of multiple different method

方法	性能
方法	分辨能力	离群值抑制	重建精度	重建效率
FISTA	↑	↑	↑	↑
SL1MMER	↑↑	↑	↑	↑
Tomo-IRENet-Raw	↑	↑	↑	↑↑↑↑
Tomo-LRENet-biU	↑↑↑	↑↑	↑↑↑	↑↑↑
Tomo-LRENet-LSTM	↑↑↑	↑↑↑	↑↑↑↑	↑↑↑↑
注：符号 ↑ 表示同一方面各方法间的相对比较，箭头数量仅表示相对优劣程度，比较仅在同一列内进行。

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表 7 SAR三维成像数据集获取途径

Table 7. SAR 3D imaging dataset acquisition methods

数据集	单位	获取途径
GOTCHA	美国空军实验室	https://www.sdms.afrl.af.mil/index.php?collection=gotcha (SDMS)
FMCW SAR	美国密歇根大学	https://github.com/AdityaMuppala/FMCW-SAR-3D-FFT (Github)
SARMV3D-Imaging-1.0	空天院	https://radars.ac.cn/web/data/getData?newsColumnId=aac4fc85-5950-432a-8ca2-bf14608d4461 (雷达学报)
SARMV3D-Imaging-2.0	空天院	https://radars.ac.cn/web/data/getData?newsColumnId=f9286c6f-a287-4e5f-be76-105b07677c3c (雷达学报)
SARMV3D-Imaging-3.0	空天院	https://radars.ac.cn/web/data/getData?newsColumnId=1cbc9f2d-f2ee-4748-9972-748c007f697f (雷达学报)
高分辨三维毫米波雷达数据集1.0	电子科技大学	https://radars.ac.cn/web/data/getData?dataType=3DRIED (雷达学报)
涪城一号SAR三维成像数据集1.0	南京航空航天大学	https://radars.ac.cn/web/data/getData?dataType=SpaceborneSAR3Dimaging (雷达学报)
DistUAV-Tomo3D-1.0	北京理工大学	https://signal.ejournal.org.cn/datasetcn?columnId=7cd9c1df-d080-4fa6-8a2d-4cd8b4010a1a (信号处理)
3DSARBuSim1.0	中山大学	https://www.scidb.cn/en/detail?dataSetId=89df9949f42d4e69958ccadf7b67f05c (电子与信息学报)

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