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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorFekry, R-
dc.creatorYao, W-
dc.creatorCao, L-
dc.creatorShen, X-
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.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:// 4.0/).en_US
dc.rightsThe 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
dc.subjectCanopy analysisen_US
dc.subjectHierarchical DBSCAN clusteringen_US
dc.subjectStrip alignmenten_US
dc.titleMarker-less UAV-LiDAR strip alignment in plantation forests based on topological persistence analysis of clustered canopy coveren_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractA 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationISPRS international journal of geo-information, May 2021, v. 10, no. 5, 284-
dcterms.isPartOfISPRS international journal of geo-information-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
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