Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation
The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algori...
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| Veröffentlicht in: | International journal of digital earth 2021-10, Vol.14 (10), p.1452-1476 |
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| creator | Pang, Yong Wang, Weiwei Du, Liming Zhang, Zhongjun Liang, Xiaojun Li, Yongning Wang, Zuyuan |
| description | The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. The method also showed good performance in a benchmark dataset with an improvement of 7% for the average matching rate. The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data. |
| doi_str_mv | 10.1080/17538947.2021.1943018 |
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But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. The method also showed good performance in a benchmark dataset with an improvement of 7% for the average matching rate. The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.</description><identifier>ISSN: 1753-8947</identifier><identifier>EISSN: 1753-8955</identifier><identifier>DOI: 10.1080/17538947.2021.1943018</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>airborne LiDAR ; Algorithms ; Approximation ; Clustering ; Computer applications ; Data ; Global navigation satellite system ; Image segmentation ; Lidar ; Matching ; Navigation ; Navigational satellites ; Nyström approximation ; Parameter estimation ; Root-mean-square errors ; sampling method ; Spectra ; spectral clustering ; Tree segmentation ; Trees</subject><ispartof>International journal of digital earth, 2021-10, Vol.14 (10), p.1452-1476</ispartof><rights>2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2021</rights><rights>2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial – No Derivatives License http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-b501d36a4bf6fcab4493c929a04173af7bf368a0ce2fe152227fa93b15ce3c83</citedby><cites>FETCH-LOGICAL-c451t-b501d36a4bf6fcab4493c929a04173af7bf368a0ce2fe152227fa93b15ce3c83</cites><orcidid>0000-0002-9760-6580</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Pang, Yong</creatorcontrib><creatorcontrib>Wang, Weiwei</creatorcontrib><creatorcontrib>Du, Liming</creatorcontrib><creatorcontrib>Zhang, Zhongjun</creatorcontrib><creatorcontrib>Liang, Xiaojun</creatorcontrib><creatorcontrib>Li, Yongning</creatorcontrib><creatorcontrib>Wang, Zuyuan</creatorcontrib><title>Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation</title><title>International journal of digital earth</title><description>The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. 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| subjects | airborne LiDAR Algorithms Approximation Clustering Computer applications Data Global navigation satellite system Image segmentation Lidar Matching Navigation Navigational satellites Nyström approximation Parameter estimation Root-mean-square errors sampling method Spectra spectral clustering Tree segmentation Trees |
| title | Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation |
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