星载光子点云与影像支持下的地形坡度自适应配准及定位精度提升

doi:10.13474/j.cnki.11-2246.2026.0713

测绘通报 ›› 2026, Vol. 0 ›› Issue (7): 88-95,117.doi: 10.13474/j.cnki.11-2246.2026.0713

星载光子点云与影像支持下的地形坡度自适应配准及定位精度提升

王帆^1,2,3, 方勇^2,3,4

1. 自然资源部第一航测遥感院, 陕西西安 710054;
2. 空间基准全国重点实验室, 陕西西安 710054;
3. 长安大学地质工程与测绘学院, 陕西西安 710054;
4. 西安测绘研究所, 陕西西安 710054

收稿日期:2025-10-20 发布日期:2026-08-15
通讯作者: 方勇。E-mail:yong.fang@vip.sina.com
作者简介:王帆(1993—),男,硕士,工程师,主要研究方向为摄影测量与遥感数据处理。E-mail:951794979@qq.com
基金资助:
基础加强计划(2021-JCJQ-ZD-066-12)

Terrain slope adaptive registration and positioning accuracy improvement based on spaceborne photon point clouds and images

Wang Fan^1,2,3, Fang Yong^2,3,4

1. The First Institute of Photogrammetry and Remote Sensing, MNR, Xi'an 710054, China;
2. State Key Laboratory of Spatial Datum, Xi'an 710054, China;
3. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China;
4. Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China

Received:2025-10-20 Published:2026-08-15

摘要/Abstract

摘要： [目的] 以ICESat-2为代表的星载光子计数激光雷达能够直接获取地表高精度三维信息,可作为一种新型控制数据用于提高卫星影像三维定位精度。为此,本文提出了一种基于地形坡度自适应广义迭代最邻近点配准的大区域网联合平差方法。[方法] 首先设计了线状分布星载光子点云与卫星影像DSM数据全局粗配准-局部精配准策略;利用三维特征检测算法提取两类异源点云共同特征点,自动筛选生成分布均匀、数量适中、质量可靠的光子控制点,采用带附加参数有理函数模型联合平差方案,提升卫星影像三维定位精度。[结果] 与完全无控定位、Google影像和SRTM DEM辅助定位相比,资源三号卫星影像的平面精度由10.445、3.695 m提升至3.058 m,高程精度由9.916、2.796 m提升至1.821 m,相较于Google影像和SRTM DEM辅助定位,平面和高程精度分别提升17.24%、34.87%;北京三号卫星影像的平面精度由6.477、3.879 m提升至2.703 m,高程精度由5.714、2.226 m提升至0.793 m,相较于Google影像和SRTM DEM辅助定位,平面和高程精度分别提升30.32%、64.38%。[结论] 本文方法能够有效提高卫星影像三维定位精度,为提升全球测图精度提供了一种可行的技术途径参考。

关键词: 星载光子控制点, 卫星立体影像, 地形坡度自适应, 广义迭代最邻近点配准, 自动筛选

Abstract: [Purposes] Spaceborne photon-counting LiDAR systems,exemplified by ICESat-2,can directly acquire high-precision 3D information about the Earth's surface.This data serves as a novel type of control information for enhancing the 3D geolocation accuracy of satellite imagery.This study proposes a large-area block adjustment method based on topographic slope adaptive generalized iterative closest point (TSA-GICP)registration. [Methods] A strategy is designed involving global coarse registration followed by local TSA-GICP fine registration between linearly distributed spaceborne photon point clouds and digital surface models (DSMs)generated from satellite imagery.A 3D feature detection algorithm is employed to extract common feature points from the two types of heterogeneous point cloud data,enabling the automatic screening and generation of photon control points (PCPs)that are uniformly distributed,moderate in quantity,and reliable in quality.A joint block adjustment scheme incorporating an additional parameter-based rational function model (RFM)is implemented. [Findings] Compared to uncontrolled positioning and positioning assisted by Google imagery and SRTM DEM,the planimetric accuracy of ZY-3 imagery improved from 10.445 and 3.695 m to 3.058 m,and the vertical accuracy improved from 9.916 and 2.796 m to 1.821 m.Compared to Google imagery and SRTM DEM-assisted positioning,the planimetric and vertical accuracies improved by 17.24% and 34.87%,respectively.For BJ-3 imagery,planimetric accuracy improved from 6.477 and 3.879 m to 2.703 m,and vertical accuracy improved from 5.714 and 2.226 m to 0.793 m.Compared to Google imagery and SRTM DEM-assisted positioning,the planimetric and vertical accuracies improved by 30.32%and 64.38%,respectively. [Conclusions] The proposed method effectively enhances the 3D geolocation accuracy of satellite imagery,providing a feasible technical reference for improving global mapping accuracy.

Key words: spaceborne photon control point, satellite stereo imagery, topographic slope adaptation, generalized iterative closest point registration, automatic screening

中图分类号:

P237

王帆, 方勇. 星载光子点云与影像支持下的地形坡度自适应配准及定位精度提升[J]. 测绘通报, 2026, 0(7): 88-95,117.

Wang Fan, Fang Yong. Terrain slope adaptive registration and positioning accuracy improvement based on spaceborne photon point clouds and images[J]. Bulletin of Surveying and Mapping, 2026, 0(7): 88-95,117.

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星载光子点云与影像支持下的地形坡度自适应配准及定位精度提升

Terrain slope adaptive registration and positioning accuracy improvement based on spaceborne photon point clouds and images

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