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

Wuming Zhang,Donghui Xie,Peng Wan,Jianbo Qi,Guangjian Yan,Hongtao Wang,Xiaoyan Wang

doi:10.3390/rs8060501

Abstract

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.

Highlights

High-resolution digital terrain models (DTMs) are critical for flood simulation, landslide monitoring, road design, land-cover classification, and forest management [1]
This method was first tested by datasets that were provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group III/3 to quantitatively test the performance of different filters and identify directions for future research [30]
The reference datasets were generated by manually filtering the Light detection and ranging (LiDAR) datasets, and each point in the samples was classified as BE or OBJ

Summary

Introduction

High-resolution digital terrain models (DTMs) are critical for flood simulation, landslide monitoring, road design, land-cover classification, and forest management [1]. Various types of filtering algorithms have been proposed to automatically extract ground points from LiDAR point clouds. Developing an automatic and easy-to-use filtering algorithm that is universally applicable for various landscapes is still a challenge. Many ground filtering algorithms have been proposed during previous decades, and these filtering methods can be mainly categorized as slope-based methods, mathematical morphology-based methods, and surface-based methods. The common assumption of slope-based algorithms is that the change in the slope of terrain is usually gradual in a neighborhood, while the change in slope between buildings or trees and the ground is very large. Remote Sens. 2016, 8, 501; doi:10.3390/rs8060501 www.mdpi.com/journal/remotesensing

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