基于几何与极化联合约束的建筑层析SAR三维成像方法

董书航; 焦泽坤; 周良将; 仇晓兰

doi:10.12000/JR25167

基于几何与极化联合约束的建筑层析SAR三维成像方法

DOI: 10.12000/JR25167 CSTR: 32380.14.JR25167

董书航^{1, 2},
焦泽坤^{1, 2, ,},
周良将^{1, 2},
仇晓兰^{1, 2}

1.
微波成像全国重点实验室北京 100190
2.
中国科学院空天信息创新研究院北京 100190

基金项目: 国家自然科学基金面上项目(62471456)，国家重点研发计划(2022YFB3901604-3)，中国科学院空天信息创新研究院科学与颠覆性技术项目(2024-AIRCAS-SDTP-01)

详细信息

作者简介:
董书航，博士，助理研究员，主要研究方向为SAR三维点云成像

焦泽坤，博士，副研究员，主要研究方向为SAR微波视觉三维成像、多维度微波成像

周良将，研究员，博士生导师，主要研究方向为合成孔径雷达系统设计与应用技术研究

仇晓兰，研究员，博士生导师，主要研究方向为SAR成像处理、SAR图像解译、新体制SAR

通讯作者:
焦泽坤 jiaozk@aircas.ac.cn

责任主编：张晓玲 Corresponding Editor: ZHANG Xiaoling

中图分类号: TP751.1
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出版历程
- 收稿日期: 2025-09-03
- 修回日期: 2025-12-14
- 网络出版日期: 2025-12-30

Tomographic SAR 3D Imaging Method Based on Geometry and Polarization Joint Constraints

DONG Shuhang^{1, 2},
JIAO Zekun^{1, 2
, ,},
ZHOU Liangjiang^{1, 2},
QIU Xiaolan^{1, 2}

1.
National Key Laboratory of Microwave Imaging Technology, Beijing 100190, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (62471456), The National Key Research and Development Program of China (2022YFB3901604-3), Science and Disruptive Technology Program (2024-AIRCAS-SDTP-01)

More Information

Corresponding author: JIAO Zekun, jiaozk@aircas.ac.cn

摘要

摘要: 层析合成孔径雷达(TomoSAR)是城市建筑物三维重建的重要技术。现有方法虽通过引入几何约束提升了成像质量，并在多极化条件下发展为极化层析SAR(PolTomoSAR)，但仍面临复杂建筑结构下几何信息依赖性强、极化模型不完善等问题。为此，该文提出一种几何与极化联合约束的TomoSAR三维成像方法，融合建筑几何结构与Pauli极化相似度信息，结合极化相干最优处理及概率密度约束，显著提升点云成像质量。实验基于机载Ku波段4通道阵列苏州实测数据，结果表明所提方法在成像精度与完整性方面均优于现有方法，验证了其有效性与优越性。
- 极化层析合成孔径雷达 /
- 几何约束 /
- 极化相似度 /
- 三维重建 /
- 点云
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.
- Polarimetric Tomographic Synthetic Aperture Radar (PolTomoSAR) /
- Geometric constraints /
- Polarimetric similarity /
- 3D Reconstruction /
- Point cloud

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图 1 无人机载阵列层析SAR的几何结构模型

Figure 1. Geometric structure model of UAV-borne array tomographic SAR

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图 2 几何与极化联合约束的分布示意图

Figure 2. Distribution with joint constraints of geometry and polarization

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图 3 极化与几何联合约束层析SAR三维成像处理流程图

Figure 3. Flowchart of tomographic SAR 3D imaging processing with joint constraints of polarization and geometry

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图 4 苏州建筑区图像

Figure 4. Image of the building area in Suzhou

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图 5 苏州建筑初始点云成像结果

Figure 5. Initial point cloud imaging results in Suzhou

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图 6 苏州建筑点云提取与聚类图像

Figure 6. Image of point cloud extraction and clustering in Suzhou

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图 7 几何约束处理结果

Figure 7. Results of geometric constraint processing

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图 8 极化信息处理结果

Figure 8. Results of polarization information processing

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图 9 几何与极化联合约束的三维点云成像结果

Figure 9. Three-dimensional point cloud imaging results with joint constraints of geometry and polarization

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图 10 点云切片结果(m)

Figure 10. Results of point cloud slicing (m)

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图 11 多种点云成像方法处理结果(旋转配准后)

Figure 11. Results using various methods (after rotation registration)

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1 约束的压缩感知三维成像流程

1. Constrained CS 3D imaging process

初始化：残差$ {r}_{0}={\boldsymbol{y}} $，y为观测向量，索引集$ S= \varnothing $，稀疏度　$ K=n $，当前解$ {x}_{0}=0 $。
步骤1：选择约束后与当前残差$ {r}_{k-1} $最相关的观测矩阵元素，
$ {j}_{k}=\arg {\max }_{j}\|{\boldsymbol{\phi}} _{j}^{{\mathrm{T}}}{r}_{k-1}f\left(\theta \right)\| $
步骤2：将选中的索引$ {j}_{k} $添加到索引集S中，$ {S}_{k}={S}_{k-1}\cup\left\{{j}_{k}\right\} $
步骤3：最小二乘法求解系数向量：　　　　 $ {{\boldsymbol{x}}}_{k}=\arg {\min }_{x}\\|{\boldsymbol{y}}-{A}_{s}x{\\|}_{2} $
即$ {{\boldsymbol{x}}}_{k}={\left({\boldsymbol{A}}_{s}^{{\mathrm{T}}}{{\boldsymbol{A}}}_{s}\right)}^{-1}{\boldsymbol{A}}_{s}^{{\mathrm{T}}}{\boldsymbol{y}} $
步骤4：更新残差：$ {r}_{k}={\boldsymbol{y}}-{{\boldsymbol{A}}}_{s}{{\boldsymbol{x}}}_{k} $
终止条件：(1) 当残差小于预设阈值：$ \\|{r}_{j}{\\|}_{2} < \epsilon $
(2) 达到设定的稀疏度：$ k=K $。

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表 1 无人机载TomoSAR与飞行参数

Table 1. UAV TomoSAR and flight parameters

参数	指标
SAR类型	阵列
频段	Ku波段
飞行高度	400 m
带宽	1200 MHz
轨道/阵列数量	4
中心下视角	45°
平均基线间隔	17.8 cm
极化通道	HH, HV, VH, VV

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表 2 点云质量定量评价参数与结果

Table 2. Point cloud quality quantitative evaluation parameters and results

成像方法	RMSE	点云完整度
无约束三维点云	3.4827	1.2966
几何与极化联合约束三维点云	2.1240	0.8800
仅几何约束三维点云	2.9644	0.7155
谱估计三维点云	3.6703	1.6566

下载: 导出CSV

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