Paper Info
Title
Comparison of Ground Point Filtering Algorithms for High-Density Point Clouds Collected by Terrestrial LiDAR
Abstract
Terrestrial LiDAR (light detection and ranging) has been used to quantify micro-topographic changes using high-density 3D point clouds in which extracting the ground surface is susceptible to off-terrain (OT) points. Various filtering algorithms are available in classifying ground and OT points, but additional research is needed to choose and implement a suitable algorithm for a given surface. This paper assesses the performance of three filtering algorithms in classifying terrestrial LiDAR point clouds: a cloth simulation filter (CSF), a modified slope-based filter (MSBF), and a random forest (RF) classifier, based on a typical use-case in quantifying soil erosion and surface denudation. A hillslope plot was scanned before and after removing vegetation to generate a test dataset of ground and OT points. Each algorithm was then tested against this dataset with various parameters/settings to obtain the highest performance. CSF produced the best classification with a Kappa value of 0.86, but its performance is highly influenced by the 'time-step' parameter. MSBF had the highest precision of 0.94 for ground point classification but the highest Kappa value of only 0.62. RF produced balanced classifications with the highest Kappa value of 0.75. This work provides valuable information in optimizing the parameters of the filtering algorithms to improve their performance in detecting micro-topographic changes.
Year
DOI
Venue
2022
10.3390/rs14194776
REMOTE SENSING
Keywords
DocType
Volume
terrestrial LiDAR, point cloud classification, cloth simulation filter, modified slope-based filter, random forest classifier, micro-topographic change detection
Journal
14
Issue
ISSN
Citations 
19
2072-4292
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Gene Bailey100.34
Yingkui Li200.34
Nathan McKinney300.34
Daniel Yoder400.68
Wesley Wright500.68
Hannah Herrero600.34