Submit Manuscript
Peer Review
Editor Work
- Home
- Articles & Issues
-
Data
- Dataset of Radar Detecting Sea
- SAR Dataset
- SARGroundObjectsTypes
- SARMV3D
- AIRSAT Constellation SAR Land Cover Classification Dataset
- 3DRIED
- UWB-HA4D
- LLS-LFMCWR
- FAIR-CSAR
- MSAR
- SDD-SAR
- FUSAR
- SpaceborneSAR3Dimaging
- Sea-land Segmentation
- SAR Multi-domain Ship Detection Dataset
- SAR-Airport
- Hilly and mountainous farmland time-series SAR and ground quadrat dataset
- SAR images for interference detection and suppression
- HP-SAR Evaluation & Analytical Dataset
- GDHuiYan-ATRNet
- Multi-System Maritime Low Observable Target Dataset
- DatasetinthePaper
- DatasetintheCompetition
- Report
- Course
- About
- Publish
- Editorial Board
- Chinese
Article Navigation >
Journal of Radars
>
2025
> Online First
| Citation: | DONG Shuhang, JIAO Zekun, ZHOU Liangjiang, et al. Tomographic SAR 3D imaging method based on geometry and polarization joint constraints[J]. Journal of Radars, in press. doi: 10.12000/JR25167
|
Tomographic SAR 3D Imaging Method Based on Geometry and Polarization Joint Constraints
DOI: 10.12000/JR25167 CSTR: 32380.14.JR25167
More Information-
Abstract
Tomographic Synthetic Aperture Radar (TomoSAR) is a key technique for 3D reconstruction of urban buildings. Although existing methods improve imaging quality by incorporating geometric constraints and have evolved into Polarimetric TomoSAR (PolTomoSAR) with multi-polarization capabilities, challenges remain in handling complex structures due to heavy reliance on geometric accuracy and limitations in polarization modeling. To address these issues, this paper proposes a novel TomoSAR 3D imaging method based on joint geometric and polarimetric constraints. The approach integrates building geometry with Pauli scattering similarity and incorporates polarization coherence optimization and probability density-based constraints to significantly enhance point cloud quality. Experiments using airborne Ku-band multi-channel SAR data over Suzhou, China, demonstrate the superiority and effectiveness of the proposed method in both accuracy and completeness of 3D reconstruction. -
-
References
[1] BROWN W M. Synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1967, AES-3(2): 217–229. doi: 10.1109/TAES.1967.5408745.[2] WILEY C A. Synthetic aperture radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 1985, AES-21(3): 440–443. doi: 10.1109/TAES.1985.310578.[3] JIAO Zekun, DING Chibiao, QIU Xiaolan, et al. Urban 3D imaging using airborne TomoSAR: Contextual information-based approach in the statistical way[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170: 127–141. doi: 10.1016/j.isprsjprs.2020.10.013.[4] JIAO Zekun, QIU Xiaolan, DONG Shuhang, et al. Preliminary exploration of geometrical regularized SAR tomography[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 201: 174–192. doi: 10.1016/j.isprsjprs.2023.05.019.[5] 李晓婉, 梁兴东, 张福博, 等. 基于几何约束移动最小二乘的TomoSAR山区点云高精度三维重建方法[J]. 雷达学报, 2022, 11(3): 363–375. doi: 10.12000/JR22049.LI Xiaowan, LIANG Xingdong, ZHANG Fubo, et al. A geometry constrained moving least squares-based high-precision 3D reconstruction method of mountains from TomoSAR point clouds[J]. Journal of Radars, 2022, 11(3): 363–375. doi: 10.12000/JR22049.[6] HAN Dong, ZHOU Liangjiang, JIAO Zekun, et al. Efficient 3D image reconstruction of airborne TomoSAR based on back projection and improved adaptive ISTA[J]. IEEE Access, 2021, 9: 47399–47410. doi: 10.1109/ACCESS.2021.3066984.[7] 丁赤飚, 仇晓兰, 徐丰, 等. 合成孔径雷达三维成像—从层析、阵列到微波视觉[J]. 雷达学报, 2019, 8(6): 693–709. doi: 10.12000/JR19090.DING Chibiao, QIU Xiaolan, XU Feng, et al. Synthetic aperture radar three-dimensional imaging—from TomoSAR and array InSAR to microwave vision[J]. Journal of Radars, 2019, 8(6): 693–709. doi: 10.12000/JR19090.[8] 仇晓兰, 焦泽坤, 杨振礼, 等. 微波视觉三维SAR关键技术及实验系统初步进展[J]. 雷达学报, 2022, 11(1): 1–19. doi: 10.12000/JR22027.QIU Xiaolan, JIAO Zekun, YANG Zhenli, et al. Key technology and preliminary progress of microwave vision 3D SAR experimental system[J]. Journal of Radars, 2022, 11(1): 1–19. doi: 10.12000/JR22027.[9] QIU Xiaolan, LUO Yitong, CHENG Yao, et al. SAR microwave vision 3D imaging dataset 3.0[EB/OL]. Journal of Radars. 2024. https://radars.ac.cn/web/data/getData?newsColumnId=2f2748db-10ef-4ad0-bcc4-f087ce59b6f8&pageType=en.[10] 仇晓兰, 罗一通, 宋舒洁, 等. 微波视觉三维SAR实验系统及其全极化数据处理方法[J]. 雷达学报(中英文), 2024, 13(5): 941–954. doi: 10.12000/JR24137.QIU Xiaolan, LUO Yitong, SONG Shujie, et al. Microwave vision three-dimensional SAR experimental system and full-polarimetric data processing method[J]. Journal of Radars, 2024, 13(5): 941–954. doi: 10.12000/JR24137.[11] DONG Shuhang, JIAO Zekun, ZHOU Liangjiang, et al. Enhanced 3D reconstruction method for polarimetric coherent optimal-based tomographic SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18: 5494–5508. doi: 10.1109/JSTARS.2024.3517843.[12] YANG Jian, PENG Yingning, and LIN Shiming. Similarity between two scattering matrices[J]. Electronics Letters, 2001, 37(3): 193–194. doi: 10.1049/el:20010104.[13] 张锐, 洪峻, 明峰. 基于极化相似度的全极化SAR自动目标识别算法[J]. 国外电子测量技术, 2010, 29(5): 24–27, 46. doi: 10.3969/j.issn.1002-8978.2010.05.005.ZHANG Rui, HONG Jun, and MING Feng. Full polarimetry SAR ATR algorithm based on polarimetry similarity[J]. Foreign Electronic Measurement Technology, 2010, 29(5): 24–27, 46. doi: 10.3969/j.issn.1002-8978.2010.05.005.[14] 徐牧, 王雪松, 肖顺平. 基于改善极化相似性的极化SAR目标增强新方法[J]. 电子与信息学报, 2008, 30(5): 1047–1051. doi: 10.3724/SP.J.1146.2007.00754.XU Mu, WANG Xuesong, and XIAO Shunping. Target enhancement in POL-SAR imagery based on the improvement of polarization characteristics similarity[J]. Journal of Electronics & Information Technology, 2008, 30(5): 1047–1051. doi: 10.3724/SP.J.1146.2007.00754.[15] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582.[16] BARANIUK R G. Compressive sensing [lecture notes][J]. IEEE Signal Processing Magazine, 2007, 24(4): 118–121. doi: 10.1109/MSP.2007.4286571.[17] DONG Shuhang, JIAO Zekun, ZHOU Liangjiang, et al. A novel filtering method of 3D reconstruction point cloud from tomographic SAR[J]. Remote Sensing, 2023, 15(12): 3076. doi: 10.3390/rs15123076.[18] WANG Chunyi, YAN Qiancheng, QIU Xiaolan, et al. A geometric semantic enhanced TomoSAR reconstruction algorithm in an urban area: Analysis and application[J]. Journal of Remote Sensing, 2025, 5: 0583. doi: 10.34133/remotesensing.0583. -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-14
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. Geometric structure model of UAV-borne array tomographic SAR
- Figure 2. Distribution with joint constraints of geometry and polarization
- Figure 3. Flowchart of tomographic SAR 3D imaging processing with joint constraints of polarization and geometry
- Figure 4. Image of the building area in Suzhou
- Figure 5. Initial point cloud imaging results in Suzhou
- Figure 6. Image of point cloud extraction and clustering in Suzhou
- Figure 7. Results of geometric constraint processing
- Figure 8. Results of polarization information processing
- Figure 9. Three-dimensional point cloud imaging results with joint constraints of geometry and polarization
- Figure 10. Results of point cloud slicing (m)
- Figure 11. Results using various methods (after rotation registration)