Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91397
PIRA download icon_1.1View/Download Full Text
Title: Marker-less UAV-LiDAR strip alignment in plantation forests based on topological persistence analysis of clustered canopy cover
Authors: Fekry, R 
Yao, W 
Cao, L
Shen, X
Issue Date: May-2021
Source: ISPRS international journal of geo-information, May 2021, v. 10, no. 5, 284
Abstract: A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn-Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.
Keywords: Canopy analysis
Forest
Hierarchical DBSCAN clustering
Strip alignment
UAV-LiDAR
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: ISPRS international journal of geo-information 
EISSN: 2220-9964
DOI: 10.3390/ijgi10050284
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
The following publication Fekry, R.; Yao,W.; Cao, L.; Shen, X. Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover. ISPRS Int. J. Geo-Inf. 2021, 10, 284 is available at https://doi.org/10.3390/ijgi10050284
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
ijgi-10-00284-v2.pdf6.38 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

6
Citations as of Jul 3, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.