An Analysis of Ground-Point Classifiers for Terrestrial LiDAR
Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research test...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/16/1915 |
| _version_ | 1798024409043173376 |
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| author | Kevin C. Roberts John B. Lindsay Aaron A. Berg |
| author_facet | Kevin C. Roberts John B. Lindsay Aaron A. Berg |
| author_sort | Kevin C. Roberts |
| collection | DOAJ |
| description | Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets: Zhang et al.’s inverted cloth simulation (CSF), Kraus and Pfeiffer’s hierarchical weighted robust interpolation classifier (HWRI), Axelsson’s progressive TIN densification filter (TIN), Evans and Hudak’s multiscale curvature classification (MCC), and Vosselman’s modified slope-based filter (MSBF). Classification performance was analyzed using the kappa index of agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons with manually classified reference datasets. The results identified a decrease in classification accuracy for the CSF and HWRI classification of low vegetation, for the HWRI and MCC classifications of variably sloped terrain, for the HWRI and TIN classifications of low outlier points, and for the TIN and MSBF classifications of off-terrain (OT) points without any ground points beneath. Additionally, the results show that while no single algorithm was suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical-morphology/slope-based method outperformed other methods, reporting a kappa score of 0.902. |
| first_indexed | 2024-04-11T18:01:57Z |
| format | Article |
| id | doaj.art-6cbb4c7b9a72470386cb09078a3e440a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:01:57Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6cbb4c7b9a72470386cb09078a3e440a2022-12-22T04:10:26ZengMDPI AGRemote Sensing2072-42922019-08-011116191510.3390/rs11161915rs11161915An Analysis of Ground-Point Classifiers for Terrestrial LiDARKevin C. Roberts0John B. Lindsay1Aaron A. Berg2Department of Geography, Environment & Geomatics, The University of Guelph, 50 Stone Rd. E, Guelph, ON N1G 2W1, CanadaDepartment of Geography, Environment & Geomatics, The University of Guelph, 50 Stone Rd. E, Guelph, ON N1G 2W1, CanadaDepartment of Geography, Environment & Geomatics, The University of Guelph, 50 Stone Rd. E, Guelph, ON N1G 2W1, CanadaPrevious literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets: Zhang et al.’s inverted cloth simulation (CSF), Kraus and Pfeiffer’s hierarchical weighted robust interpolation classifier (HWRI), Axelsson’s progressive TIN densification filter (TIN), Evans and Hudak’s multiscale curvature classification (MCC), and Vosselman’s modified slope-based filter (MSBF). Classification performance was analyzed using the kappa index of agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons with manually classified reference datasets. The results identified a decrease in classification accuracy for the CSF and HWRI classification of low vegetation, for the HWRI and MCC classifications of variably sloped terrain, for the HWRI and TIN classifications of low outlier points, and for the TIN and MSBF classifications of off-terrain (OT) points without any ground points beneath. Additionally, the results show that while no single algorithm was suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical-morphology/slope-based method outperformed other methods, reporting a kappa score of 0.902.https://www.mdpi.com/2072-4292/11/16/1915LiDARterrestrial LiDAR (TLS)ground point classification (GPC)ground point filterground point separationpoint cloud classification |
| spellingShingle | Kevin C. Roberts John B. Lindsay Aaron A. Berg An Analysis of Ground-Point Classifiers for Terrestrial LiDAR Remote Sensing LiDAR terrestrial LiDAR (TLS) ground point classification (GPC) ground point filter ground point separation point cloud classification |
| title | An Analysis of Ground-Point Classifiers for Terrestrial LiDAR |
| title_full | An Analysis of Ground-Point Classifiers for Terrestrial LiDAR |
| title_fullStr | An Analysis of Ground-Point Classifiers for Terrestrial LiDAR |
| title_full_unstemmed | An Analysis of Ground-Point Classifiers for Terrestrial LiDAR |
| title_short | An Analysis of Ground-Point Classifiers for Terrestrial LiDAR |
| title_sort | analysis of ground point classifiers for terrestrial lidar |
| topic | LiDAR terrestrial LiDAR (TLS) ground point classification (GPC) ground point filter ground point separation point cloud classification |
| url | https://www.mdpi.com/2072-4292/11/16/1915 |
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