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An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Remote sensing (Basel, Switzerland), 2016-06, Vol.8 (6), p.501 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8060501

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  • Title:
    An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation
  • Author: Zhang, Wuming ; Qi, Jianbo ; Wan, Peng ; Wang, Hongtao ; Xie, Donghui ; Wang, Xiaoyan ; Yan, Guangjian
  • Subjects: cloth simulation ; ground filtering algorithm ; LiDAR point cloud
  • Is Part Of: Remote sensing (Basel, Switzerland), 2016-06, Vol.8 (6), p.501
  • Description: Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.
  • Publisher: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs8060501
  • Source: AUTh Library subscriptions: ProQuest Central
    Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
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