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Improve topographic LiDAR point cloud interpolation accuracy with geodesic distance

文献类型：期刊论文

作者Duan, Xinqiao2,3; Ge, Yong1,3; He, Haiqing2
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2025-10-01
卷号328页码:114900
关键词Topographic point cloud Interpolation Geodesic distance Dimensionality reduction Domain decomposition Point cloud distance
ISSN号0034-4257
DOI10.1016/j.rse.2025.114900
产权排序3
文献子类Article
英文摘要High-precision topographic LiDAR point clouds provide a concrete elevation basis for many environmental and ecological applications, but they suffer from distinctly uneven density with voids of varying sizes. Interpolation tools most commonly serve to resample the density and fill the voids under the designated scale of the application. However, the target spaces in which the topographic point clouds reside are essentially non-Euclidean surfaces; the true distances between sample points are curved geodesic distances, which differ significantly from the conventionally used Euclidean distances, so classical interpolation models should be investigated for potential systematic biases. First, we introduce geodesic distance to topographic point clouds as a dimensionality reduction constraint to project the point cloud. This ensures the positive definiteness of the autocorrelation matrix and the revision of conventional interpolation algorithms. Then, we carried out a benchmark interpolation with deterministic and geostatistical models on a relatively regular topographic point cloud. Product point cloud resampling was subsequently examined with the optimal method. In response to the computational challenge, we devise a feature-points embedded domain decomposition calculation and propose a cross-validation-based point-to-point distance for better evaluation of the interpolation accuracy. The experimental results with different scenarios show substantial improvement in interpolation accuracy with the introduction of geodesic distance, which is of universal significance in prompting the precise utilization of topographic models. The related data and code will be open-sourced in the community.
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WOS关键词GENERATION
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001529920100002
出版者ELSEVIER SCIENCE INC
源URL[http://ir.igsnrr.ac.cn/handle/311030/215429]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Ge, Yong
作者单位1.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Peoples R China;
2.East China Univ Technol, Sch Surveying & Geoinformat Engn, Nanchang, Peoples R China;
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Duan, Xinqiao,Ge, Yong,He, Haiqing. Improve topographic LiDAR point cloud interpolation accuracy with geodesic distance[J]. REMOTE SENSING OF ENVIRONMENT,2025,328:114900.
APA Duan, Xinqiao,Ge, Yong,&He, Haiqing.(2025).Improve topographic LiDAR point cloud interpolation accuracy with geodesic distance.REMOTE SENSING OF ENVIRONMENT,328,114900.
MLA Duan, Xinqiao,et al."Improve topographic LiDAR point cloud interpolation accuracy with geodesic distance".REMOTE SENSING OF ENVIRONMENT 328(2025):114900.

入库方式： OAI收割

来源：地理科学与资源研究所

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