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Open AccessArticle

An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data

1
School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, 159 Longpan road, Nanjing 210037, China
4
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
5
Institute of Space Weather, School of Math & Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1999; https://doi.org/10.3390/rs10121999
Received: 5 November 2018 / Revised: 4 December 2018 / Accepted: 8 December 2018 / Published: 10 December 2018
(This article belongs to the Special Issue Aerial and Near-Field Remote Sensing Developments in Forestry)
Forests play a key role in terrestrial ecosystems, and the variables extracted from single trees can be used in various fields and applications for evaluating forest production and assessing forest ecosystem services. In this study, we developed an automated hierarchical single-tree segmentation approach based on the high density three-dimensional (3D) Unmanned Aerial Vehicle (UAV) point clouds. First, this approach obtains normalized non-ground UAV points in data preprocessing; then, a voxel-based mean shift algorithm is used to roughly classify the non-ground UAV points into well-detected and under-segmentation clusters. Moreover, potential tree apices for each under-segmentation cluster are obtained with regard to profile shape curves and finally input to the normalized cut segmentation (NCut) algorithm to segment iteratively the under-segmentation cluster into single trees. We evaluated the proposed method using datasets acquired by a Velodyne 16E LiDAR system mounted on a multi-rotor UAV. The results showed that the proposed method achieves the average correctness, completeness, and overall accuracy of 0.90, 0.88, and 0.89, respectively, in delineating single trees. Comparative analysis demonstrated that our method provided a promising solution to reliable and robust segmentation of single trees from UAV LiDAR data with high point cloud density. View Full-Text
Keywords: UAV LiDAR; single tree; segmentation; mean shift; improved normalized cut UAV LiDAR; single tree; segmentation; mean shift; improved normalized cut
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MDPI and ACS Style

Yan, W.; Guan, H.; Cao, L.; Yu, Y.; Gao, S.; Lu, J. An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data. Remote Sens. 2018, 10, 1999.

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