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Article

An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation

by 1,2, 1,*, 1, 2, 1 and 1
1
Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China
2
Hainan Geomatics Centre, National Administration of Surveying, Mapping and Geoinformation of China, Haikou 570203, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(1), 172; https://doi.org/10.3390/s19010172
Received: 8 November 2018 / Revised: 24 December 2018 / Accepted: 2 January 2019 / Published: 5 January 2019
(This article belongs to the Section Remote Sensors)
Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing. View Full-Text
Keywords: LiDAR; segmentation; DBSCAN; parameter estimation LiDAR; segmentation; DBSCAN; parameter estimation
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MDPI and ACS Style

Wang, C.; Ji, M.; Wang, J.; Wen, W.; Li, T.; Sun, Y. An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation. Sensors 2019, 19, 172. https://doi.org/10.3390/s19010172

AMA Style

Wang C, Ji M, Wang J, Wen W, Li T, Sun Y. An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation. Sensors. 2019; 19(1):172. https://doi.org/10.3390/s19010172

Chicago/Turabian Style

Wang, Chunxiao, Min Ji, Jian Wang, Wei Wen, Ting Li, and Yong Sun. 2019. "An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation" Sensors 19, no. 1: 172. https://doi.org/10.3390/s19010172

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