{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:31:34Z","timestamp":1760369494221,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T00:00:00Z","timestamp":1543536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671449"],"award-info":[{"award-number":["41671449"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few studies investigated the potential of both intensity and color on classification using TLS point cloud. In this paper, the geometric features, color features, and intensity features were extracted based on a supervoxel neighborhood. In addition, the original intensity was also corrected for range effect, which is why the corrected intensity features were also extracted. The different combinations of these features were tested on four real-world data sets. Experimental results demonstrate that both color and intensity features can complement the geometric features to help improve the classification results. Furthermore, the combination of geometric features, color features, and corrected intensity features together achieves the highest accuracy in our test.<\/jats:p>","DOI":"10.3390\/s18124206","type":"journal-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T12:13:17Z","timestamp":1543579997000},"page":"4206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Comparison of Different Feature Sets for TLS Point Cloud Classification"],"prefix":"10.3390","volume":"18","author":[{"given":"Quan","family":"Li","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.cad.2009.02.010","article-title":"3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields","volume":"41","author":"Lim","year":"2009","journal-title":"Comput. Aided Des."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4172","DOI":"10.1080\/01431161.2016.1211348","article-title":"A supervoxel-based spectro-spatial approach for 3d urban point cloud labelling","volume":"37","author":"Ramiya","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1109\/JSTARS.2015.2394803","article-title":"3-D point cloud object detection based on supervoxel neighborhood with Hough forest framework","volume":"8","author":"Wang","year":"2015","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Plaza-Leiva, V., Gomez-Ruiz, J.A., Mandow, A., and Garc\u00eda-Cerezo, A. (2017). Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning. Sensors, 17.","DOI":"10.3390\/s17030594"},{"key":"ref_6","first-page":"183","article-title":"A hybrid semantic point cloud classification\u2013segmentation framework based on geometric features and semantic rules","volume":"85","author":"Weinmann","year":"2017","journal-title":"PFG Photogramm. Remote Sens. Geoinf."},{"key":"ref_7","first-page":"259","article-title":"Assessing the possibility of land-cover classification using LIDAR intensity data","volume":"34","author":"Song","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1109\/LGRS.2013.2251453","article-title":"An object-based approach for urban land cover classification: Integrating LiDAR height and intensity data","volume":"10","author":"Zhou","year":"2013","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.3390\/rs5083749","article-title":"Svm-based classification of segmented airborne lidar point clouds in urban areas","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2013.11.005","article-title":"Using mobile laser scanning data for automated extraction of road markings","volume":"87","author":"Guan","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","first-page":"125","article-title":"Automated road markings extraction from mobile laser scanning data","volume":"32","author":"Kumar","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, D., Ying, S., and Li, Y. (2016). Recognition and reconstruction of zebra crossings on roads from mobile laser scanning data. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5070125"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"177","DOI":"10.5194\/isprs-annals-III-3-177-2016","article-title":"Fast semantic segmentation of 3D point clouds with strongly varying density","volume":"III-3","author":"Hackel","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"94094","DOI":"10.1117\/1.JRS.9.094094","article-title":"Intensity data correction based on incidence angle and distance for terrestrial laser scanner","volume":"9","author":"Tan","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5412","DOI":"10.1109\/TGRS.2016.2564501","article-title":"A three-step approach for TLS point cloud classification","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.3390\/rs5041624","article-title":"Segmentation based classification of 3D urban point clouds: A super-voxel based approach with evaluation","volume":"5","author":"Aijazi","year":"2013","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2018.05.004","article-title":"Toward better boundary preserved supervoxel segmentation for 3D point clouds","volume":"143","author":"Lin","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Papon, J., Abramov, A., Schoeler, M., and Worgotter, F. (2013, January 23\u201328). Voxel cloud connectivity segmentation-supervoxels for point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.264"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.04.018","article-title":"A probabilistic graphical model for the classification of mobile LiDAR point clouds","volume":"143","author":"Kang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.isprsjprs.2007.05.008","article-title":"Correction of laser scanning intensity data: Data and model-driven approaches","volume":"62","author":"Hofle","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kashani, A., Olsen, M., Parrish, C., and Wilson, N. (2015). A review of lidar radiometric processing: From AD HOC intensity correction to rigorous radiometric calibration. Sensors, 15.","DOI":"10.3390\/s151128099"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.3390\/rs3102207","article-title":"Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: Search for correction methods","volume":"3","author":"Kaasalainen","year":"2011","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1109\/TGRS.2014.2330852","article-title":"Intensity correction of terrestrial laser scanning data by estimating laser transmission function","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tan, K., and Cheng, X. (2016). Correction of incidence angle and distance effects on tls intensity data based on reference targets. Remote Sens., 8.","DOI":"10.3390\/rs8030251"},{"key":"ref_25","unstructured":"Jelalian, A.V. (1992). Laser Radar Systems, Artech House."},{"key":"ref_26","first-page":"99","article-title":"Streamed vertical rectangle detection in terrestrial laser scans for facade database production","volume":"1","author":"Vallet","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"181","DOI":"10.5194\/isprsannals-II-3-181-2014","article-title":"Semantic 3d scene interpretation: A framework combining optimal neighborhood size selection with relevant features","volume":"2","author":"Weinmann","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","first-page":"207","article-title":"Airborne LiDAR feature selection for urban classification using random forests. Int. Arch. Photogramm","volume":"38","author":"Chehata","year":"2009","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.isprsjprs.2017.05.012","article-title":"Joint classification and contour extraction of large 3D point clouds","volume":"130","author":"Hackel","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Weinmann, M., Weinmann, M., Mallet, C., and Br\u00e9dif, M. (2017). A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas. Remote Sens., 9.","DOI":"10.3390\/rs9030277"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Q., and Cheng, X. (2018). Damage Detection for Historical Architectures Based on TLS Intensity Data. ISPRS Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42.","DOI":"10.5194\/isprs-archives-XLII-3-915-2018"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4206\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:33:40Z","timestamp":1760196820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,30]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["s18124206"],"URL":"https:\/\/doi.org\/10.3390\/s18124206","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,11,30]]}}}