A feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction. Issue 377 (4th March 2021)

• Record Type: Journal Article
• Authors: Chen, Chuanfa; Gao, Yuan; Li, Yanyan
• Is Part Of: Survey review. Volume 53:Issue 377(2021)
• Page Start: 146
• Page End: 157
• Publication Date: 2021-03-04
• Subjects: Denoise -- DEM -- LiDAR -- Interpolation; Surveying -- Periodicals; Great Britain -- Surveys -- Periodicals; 526.9
• DOI: 10.1080/00396265.2019.1704562
• Languages: English
• ISSNs: 0039-6265

Abstract

Abstract : To attenuate positional errors of LiDAR-derived datasets for constructing digital elevation models (DEMs), a feature-preserving point denoising algorithm (F-PDA) is developed in this paper. F-PDA includes three main steps: surface normal estimation, normal filtering and point position update. Numerical tests with two simulated surfaces indicate that F-PDA is always more accurate than kriging and natural neighbour. Furthermore, F-PDA has a high effectiveness of preserving feature lines. Real-world examples of interpolating LiDAR samples demonstrate that F-PDA can best retain both prominent and subtle terrain features, while faithfully removing errors in mountainous and flat regions. Moreover, it outperforms some well-known interpolation methods.