众源数据辅助的高分辨率 SAR 影像控制点自动提取与校正方法研究

向俞明; 白植达; 陈锦杨; 洪中华; 刘世杰; 童小华

doi:10.12000/JR25141

众源数据辅助的高分辨率 SAR 影像控制点自动提取与校正方法研究

DOI: 10.12000/JR25141 CSTR: 32380.14.JR25141

向俞明^{1, 2},
白植达¹,
陈锦杨³,
洪中华^3, ,,
刘世杰^{1, 2},
童小华^{1, 2}

1.
同济大学测绘与地理信息学院上海 200092
2.
上海市航天测绘遥感与空间探测重点实验室上海 200092
3.
上海海洋大学信息学院上海 201306

基金项目: 国家自然科学基金(42221002, 42171432)，上海市科技计划项目(2024CSJZN01300)，中央高校基本科研业务费专项资金资助

详细信息

作者简介:
向俞明，博士，副教授，主要研究方向为SAR影像配准、遥感影像高精度定位测图

白植达，博士生，主要研究方向为遥感影像定位平差

陈锦杨，硕士生，主要研究方向为多模态影像匹配

洪中华，博士，教授，主要研究方向为遥感影像全球测图

刘世杰，博士，教授，主要研究方向为航天精准测绘遥感

童小华，博士，教授/中国工程院院士，主要研究方向为航天测绘遥感与深空探测

通讯作者:
洪中华 zhhong@shou.edu.cn

责任主编：张红 Corresponding Editor: ZHANG Hong
中图分类号: TP751
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出版历程

Research on Automatic Control Point Extraction and Correction Method for High-Resolution Synthetic Aperture Radar Imagery Assisted by Multi-source Data

XIANG Yuming^{1, 2},
BAI Zhida¹,
CHEN Jinyang³,
HONG Zhonghua^{3
, ,},
LIU Shijie^{1, 2},
TONG Xiaohua^{1, 2}

1.
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
2.
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Shanghai 200092, China
3.
College of Information Science and Technology, Shanghai Ocean University, Shanghai 201306, China

Funds: National Natural Science Foundation of China (42221002, 42171432),Shanghai Science and Technology Program Project (2024CSJZN01300), Special Fund for Basic Scientific Research Business Expenses of Central Universities

More Information

Corresponding author: HONG Zhonghua

摘要

摘要: 控制点作为遥感影像定位精度提升的核心基准，其空间分布特征与几何质量直接影响正射校正的可靠性，是保障多源影像融合、变化检测及定量反演精度的关键基础。针对传统角反射器布设成本高、实施难度大，难以满足全球应用需求的问题，以及现有异源控制点(如光学影像和激光测高数据)因与合成孔径雷达(SAR)影像存在显著模态差异，难以兼顾精度与鲁棒性的问题，该研究提出一种基于众源数据的高分辨率SAR影像控制点自动提取方法，并构建了基于控制片的高精度正射校正技术框架。本方法充分利用城市环境中广泛分布的杆状人造地物特性：在光学影像中，这类地物呈现本体-阴影协同结构特征，在SAR影像中则表现为十字形强散射响应。首先利用开源机场跑道数据校正谷歌光学影像，建立平面基准框架；进而通过光学-SAR影像匹配点与立体SAR影像匹配点的协同平差解算，实现不同升降轨立体SAR影像的初始定位优化；最终结合道路和停车场矢量数据提取感兴趣区域，采用信杂比检测算法识别强散射点，并通过点目标分析与立体定位技术获取控制点的三维空间坐标。基于控制点坐标对立体SAR影像的残余平面误差进行修正后，可生成不同升降轨SAR影像的控制片数据。通过采用多个区域的高分三号升降轨SAR影像开展控制点提取验证，结果表明：基于聚束模式立体SAR影像提取的控制点三维定位精度达亚米级，基于提取的控制点和控制片实现了测试影像的正射校正，通过角反射器和机载激光点云真值验证，结果发现测试影像定位精度提升显著，其中聚束模式定位误差为1.78像素，超精细条带模式为1.09像素，精细条带模式为0.82像素，较传统光学参考影像匹配校正方法精度提升了47.2％, 49.3％和37.4％。本研究创新性地引入众源信息辅助SAR控制点提取和升降轨SAR控制片构建，突破光学参考底图匹配校正的精度瓶颈，为高分辨率SAR影像精确定位与联合处理提供了可推广的新路径。
- 合成孔径雷达 /
- 控制点 /
- SAR控制片 /
- 正射校正 /
- 众源辅助数据
Abstract: Ground control points (GCPs) are essential for improving the positioning accuracy of remote sensing imagery. Their spatial distribution and geometric quality directly affect the reliability of orthorectification. GCPs serve as a critical foundation for ensuring the accuracy of multi-source image fusion, change detection, and quantitative inversion. However, traditional corner reflector deployment presents high costs and implementation difficulties, struggling to meet global application demands. Additionally, existing heterogeneous control points (such as optical imagery and laser altimetry data) exhibit significant modal differences relative to synthetic aperture radar (SAR) imagery, which affects their ability to balance accuracy and robustness. To address these challenges, this study proposes an automatic control point extraction method for high-resolution SAR imagery based on multi-source data. Furthermore, a high-precision orthorectification framework is established using control chips. The method leverages the characteristics of widely distributed pole-like artificial features in urban environments: these features exhibit a body–shadow collaborative structure in optical imagery and a cross-shaped strong scattering response in SAR imagery. First, open-source airport runway data are used to correct Google optical imagery, establishing a planar reference framework. Next, initial positioning optimization for stereo SAR images from ascending and descending orbits is achieved by jointly adjusting optical-SAR and stereo SAR image matching points. Finally, road and parking lot vector data are utilized to extract regions of interest, where strong scattering points are identified using a signal-to-clutter ratio detection algorithm. Three-dimensional spatial coordinates of control points are obtained via point target analysis and stereo positioning techniques. After correcting residual planar errors in stereo SAR images using control point coordinates, control chip data for ascending and descending orbit SAR images are generated. Validation experiments using GaoFen-3 SAR images from multiple regions show that the 3D positioning accuracy of control points extracted from spotlight mode stereo SAR imagery reaches the submeter level. Orthorectification of test images using extracted control points and control chips significantly improves positioning accuracy, as verified by corner reflectors and airborne LiDAR point cloud-based ground truth. Positioning errors are 1.78 pixels (spotlight mode), 1.09 pixels (ultrafine stripmap mode), and 0.82 pixels (fine stripmap mode), corresponding to improvements of 47.2%, 49.3%, and 37.4%, respectively, compared to traditional optical reference image matching correction methods. This study introduces crowdsourced information to assist SAR control point extraction and ascending/descending orbit SAR control chip construction, overcoming the accuracy limitations of optical reference image matching correction. The proposed method provides a scalable approach for high-precision positioning and joint processing of high-resolution SAR imagery.
- SAR (Synthetic Aperture Radar) /
- GCP (Ground Control Point) /
- Control Chip of SAR Imagery /
- Orthorectification /
- Multi-source Data

HTML全文

图 1 众源数据辅助的SAR影像控制点自动提取算法流程图。

Figure 1. Flowchart of the automatic extraction algorithm for GCP in SAR images assisted by multi-sourced data

下载: 全尺寸图片幻灯片

图 2 测试区域众源数据分布图。

Figure 2. Distribution map of multi-sourced data in the study area

下载: 全尺寸图片幻灯片

图 3 本项目控制点构造对象—杆状结构图示(如红色矩形框)。

Figure 3. The rod-shaped structures used as control points in this project are indicated by red rectangles.

下载: 全尺寸图片幻灯片

图 4 基于控制片的SAR影像正射校正算法流程图。

Figure 4. Flowchart of the orthorectification algorithm for SAR imagery based on control chips.

下载: 全尺寸图片幻灯片

图 5 火奴鲁鲁(ID1)的升降轨控制片结果。

Figure 5. Results of control chips for ascending and descending orbits in test area ID1.

下载: 全尺寸图片幻灯片

图 6 安克雷奇(ID2)的升降轨控制片结果。

Figure 6. Results of control chips for ascending and descending orbits in test area ID2.

下载: 全尺寸图片幻灯片

图 7 马歇尔(ID3)的升降轨控制片结果。

Figure 7. Results of control chips for ascending and descending orbits in test area ID3.

下载: 全尺寸图片幻灯片

图 8 萨斯卡通(ID4)的升降轨控制片结果。

Figure 8. Results of control chips for ascending and descending orbits in test area ID4.

下载: 全尺寸图片幻灯片

图 9 兰开斯特(ID5)的升降轨控制片结果。

Figure 9. Results of control chips for ascending and descending orbits in test area ID5.

下载: 全尺寸图片幻灯片

图 10 待校正的SAR影像和用于评估的真值点位。

Figure 10. SAR images to be rectified and ground truth points for evaluation.

(a) Honolulu test area data and its ground truth distribution (b) Lancaster test area data and its ground truth distribution

下载: 全尺寸图片幻灯片

图 11 SAR影像校正结果的箱线图。

Figure 11. Box plot of SAR image correction results.

下载: 全尺寸图片幻灯片

表 1 测试区域和众源数据信息。

Table 1. The study area and multi-sourced data information.

测试区域	开源机场跑道	道路和停车场矢量	DEM	谷歌光学影像	高分三号C波段立体SAR影像
火奴鲁鲁(ID1)	Airport Data (FAA)	OpenStreet Map	≈10 m USGS	≈0.3 m RGB	聚束/降轨/右侧视/38.2°/0.56 m/0.34 m	聚束/升轨/右侧视/36.7°/0.56 m/0.34 m
安克雷奇(ID2)					聚束/降轨/右侧视/35.4°/0.56 m/0.34 m	聚束/升轨/右侧视/34.5°/0.56 m/0.33 m
马歇尔(ID3)					聚束/降轨/右侧视/32.3°/0.56 m/0.33 m	聚束/升轨/右侧视/27.7°/0.56 m/0.31 m
萨斯卡通(ID4)					聚束/降轨/右侧视/26.6°/0.56 m/0.31 m	聚束/升轨/右侧视/39.1°/0.56 m/0.34 m
兰开斯特(ID5)					条带/降轨/右侧视/28.4°/1.12 m/1.73 m	条带/升轨/右侧视/42.5°/1.12 m/1.7 m

下载: 导出CSV

表 2 测试区域提取到的控制点精度评估结果，以及控制点数量

Table 2. Accuracy assessment results of extracted control points in the test areas, and the number of control points

测试区域	平面精度RMSE_h	高程精度RMSE_v	N_GCP
火奴鲁鲁(ID1)	0.85 m	0.47 m	124
安克雷奇(ID2)	0.87 m	0.26 m	26
马歇尔(ID3)	0.69 m	0.31 m	239
萨斯卡通(ID4)	0.55 m	0.26 m	53
兰开斯特(ID5)	1.35 m	1.02 m	155

下载: 导出CSV

表 3 待校正的SAR影像成像参数信息。

Table 3. The imaging parameter information of the SAR image to be rectified.

测试区域	卫星型号	成像模式	成像视角	轨道/侧视方向	采样间隔	影像尺寸(像素)	评估真值
火奴鲁鲁(ID1-1)	高分三号	聚束	31.8°	降轨/右	0.56m/0.33m	12148×32226	机载激光点云
火奴鲁鲁ID1-2)		聚束	39.6°	降轨/左	0.56m/0.34m	16672×29190	机载激光点云
兰开斯特(ID5-1)		超精细条带	28.4°	升轨/右	1.12m/1.73m	14297×21550	角反射器
兰开斯特(ID5-2)		精细条带II	39.2°	降轨/右	2.25m/4.82m	32026×24656	角反射器

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表 4 所有算法在4组测试影像上的定位误差(RMSE，像素)以及平均处理时间

Table 4. Positioning errors (RMSE, pixels) and average processing time of all algorithms on four sets of test images

测试区域	本文算法	基于SAR原始定位参数和DEM校正	升降轨SAR连接点匹配平差校正	SFOC²⁴	G2L¹⁸
火奴鲁鲁(ID1-1)	2.07	27.37	15.39	4.08	3.18
火奴鲁鲁(ID1-2)	1.55	19.24	10.36	2.78	2.12
兰开斯特(ID5-1)	1.09	28.77	14.16	2.15	1.50
兰开斯特(ID5-2)	0.82	13.37	4.35	1.31	1.83
平均处理时间(s)	72.3	14.5	65.9	57.2	85.9

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表 5 消融实验对比结果，包括控制点平面精度(m)和高程精度(m)，w/o代表去除

Table 5. The comparison results of ablation study, including positioning error (m) and elevation error (m)

消融方法	本文算法	w/o 机场跑道坐标	w/o 连接点平差	w/o 立体交会解算
平面精度RMSE_h	0.85	5.19	1.92	1.68
高程精度RMSE_v	0.47	1.44	1.35	1.52

下载: 导出CSV

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