In the surveying and geographic information industry, it is extremely important to match control points to terrain data such as DSM. After matching the measurement control points with DSM,the position and elevation in the high-precision DSM digital model can be used to analyze and evaluate the quality of the measurement control points. Conversely, the coordinates of high-precision measurement control points can be used to correct the plane position and elevation in DSM, making them consistent with the coordinates of the control points and more accurately reflecting surface information. This study mainly adopts a method that takes into account global elevation differences and can achieve matching between measurement control points and DSM while minimizing global elevation differences.The algorithm designed in this article is based on principles such as affine transformation,and effectively solves the main problem of traditional control point and DSM matching by measuring the root mean square error (RMSE)of elevation anomalies between control point data and DSM terrain data,achieving high-precision matching between measurement control points and DSM.The main research content of this article is as follows: (1) We used satellite laser point cloud control point data and DEM terrain data released by the United States Geological Survey. After a brief introduction, we carried out preprocessing steps such as extracting high-precision control point data and converting the plane and elevation benchmarks of the two types of data. (2) The experimental data was divided into three groups of test data, and the matching results were obtained using the designed matching method. The results were compared with the single point matching experimental group as a control. The analysis showed that the matching method that takes into account the minimum global elevation difference has a significant improvement in matching accuracy, and the improvement is related to the terrain characteristics of the study area.

Digital Surface Model;Control points;Affine transformation;Global elevation difference;Root mean square error;LiDAR point cloud

[1] LIU Shao-feng, Wang Tao, ZHang Hui-ping, et al.Application of digital elevation model in the study of surface processes [J].Earth Science Frontier, 2005, (01): 303-309. [2] Lai Wenyi. Study on Block Adjustment Method of Satellite Photon Counting Point Cloud Combined with Satellite Image [D]. Chang’an University, 2022. DOI: 10. 26976/d. CNKI. Gchau. 2022. 000774. [3] ZHang Fan, HUang Xianfeng, LI Deren. Research progress of laser scanning and optical image data registration [J]. Bulletin of Surveying and Mapping, 2008, (02): 7-10. [4] Zhong Cheng, Li Hui, Huang Xianfeng, et al. Automatic registration of LiDAR data and aerial images using 6-tuple relaxation method [J]. Journal of Wuhan University (Information Science Edition), 2009,34 (12): 1426-1430. [5] ZHang Xinlei, XING Shuai, XU Qing, et al. Joint block adjustment of ATLAS data and ZY-3 02 image [J]. Infrared and Laser Engineering, 2020, 49 (S2): 155-162. [6] LI Xiao-ling. Research on key technology of small scale DSM production based on radar point cloud [J]. Surveying and Mapping and Spatial Geographic Information, 2023, 46 (08): 197-200. [7] CHen Xiang-guang, ZHang Yong-jun. Automatic extraction method of DSM from Gaojing 1 satellite image [J]. Surveying and Mapping Geographic Information, 2019, 44 (05): 11-15. DOI: 10. 14188/j.2095-6045. 2018111. [8] Jin Xuebao, Zhou Fengsong, Zhang Yubing. DSM data production and accuracy verification based on GF7 satellite image [J]. Land and Resources Guide, 2023, 20 (02): 18-22. [9] Xiang Jianbing, Lv Xiaolei, Fu Xikai, et al. InSAR imaging and DSM generation technology of TX-2 [J]. Chinese Journal of Surveying and Mapping, 2022, 51 (12): 2493-2500. [10]Li Yongjie, Sun Zhongchang, Wei Lideng, et al. Generation of DOM and DSM in high mountain areas of Sichuan using airborne dual-antenna millimeter-wave InSAR [J]. Bulletin of Surveying and Mapping, 2020, (10): 31-37. DOI: 10. 13474/J. CNKI. 11-2246.2020.0314. [11]Pan Xuechen, Jiang Ting, Yu Anzhu, et al. Geometrical positioning of remote sensing images aided by reference image data [J]. Journal of Remote Sensing, 2019, 23 (04): 673-684. [12]Ke Min, Tao Zhen, Zhang Dalong. A Method for Quickly Obtaining Port Control Point Coordinates [J]. Surveying and Mapping Geographic Information, 2022, 47 (05): 30-33. DOI: 10. 14188/j.2095-6045. 2020547. [13]Wang Ruopu, Wu Mei, An Jinwen. Control Point Coordinate Acquisition Based on Aerial Image Sequence [J]. Journal of Projectile and Guidance, 2005, (S6): 231-233. [14]Cai Chao. Research on Satellite Image Digital Elevation Model Extraction Based on ICESat-2 Point Cloud Data [D]. Chang’an University, 2020. [15]Pan Xuechen, Jiang Ting, Yu Anzhu, et al. Block adjustment of high-resolution remote sensing images aided by Google Earth [J]. Journal of Surveying and Mapping Science and Technology, 2017,34 (06): 622-627.

DOI： https://doi.org/10.62639/sspjiess19.20250202

Published： 2025