{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:08:57Z","timestamp":1774030137874,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2013,3,28]],"date-time":"2013-03-28T00:00:00Z","timestamp":1364428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.<\/jats:p>","DOI":"10.3390\/rs5041624","type":"journal-article","created":{"date-parts":[[2013,3,28]],"date-time":"2013-03-28T13:35:44Z","timestamp":1364477744000},"page":"1624-1650","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":145,"title":["Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation"],"prefix":"10.3390","volume":"5","author":[{"given":"Ahmad","family":"Aijazi","sequence":"first","affiliation":[{"name":"Institut Pascal, Clermont Universit\u00e9, Universit\u00e9 Blaise Pascal, BP 10448, F-63000 Clermont-Ferrand, France"},{"name":"Institut Pascal, CNRS, UMR 6602, F-63171 Aubi\u00e8re, France"}]},{"given":"Paul","family":"Checchin","sequence":"additional","affiliation":[{"name":"Institut Pascal, Clermont Universit\u00e9, Universit\u00e9 Blaise Pascal, BP 10448, F-63000 Clermont-Ferrand, France"},{"name":"Institut Pascal, CNRS, UMR 6602, F-63171 Aubi\u00e8re, France"}]},{"given":"Laurent","family":"Trassoudaine","sequence":"additional","affiliation":[{"name":"Institut Pascal, Clermont Universit\u00e9, Universit\u00e9 Blaise Pascal, BP 10448, F-63000 Clermont-Ferrand, France"},{"name":"Institut Pascal, CNRS, UMR 6602, F-63171 Aubi\u00e8re, France"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2004.05.004","article-title":"Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds","volume":"59","author":"Sithole","year":"2004","journal-title":"ISPRS J. 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Nara, Japan."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient graph-based image segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vision"},{"key":"ref_8","unstructured":"Zhu, X., Zhao, H., Liu, Y., Zhao, Y., and Zha, H (2010, January 18\u201322). Segmentation and Classification of Range Image from an Intelligent Vehicle in Urban Environment. Taipei, Taiwan."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Triebel, R., Shin, J., and Siegwart, R (2010, January 27\u201330). Segmentation and Unsupervised Part-Based Discovery of Repetitive Objects. Zaragoza, Spain.","DOI":"10.15607\/RSS.2010.VI.009"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schoenberg, J., Nathan, A., and Campbell, M (2010, January 18\u201322). Segmentation of Dense Range Information in Complex Urban Scenes. Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651749"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Strom, J., Richardson, A., and Olson, E (2010, January 18\u201322). Graph-Based Segmentation for Colored 3D Laser Point Clouds. Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5650459"},{"key":"ref_12","unstructured":"Pauling, F., Bosse, M., and Zlot, R (2009, January 2\u20134). Automatic Segmentation of 3D Laser Point Clouds by Ellipsoidal Region Growing. Sydney, Australia."},{"key":"ref_13","unstructured":"Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., and Ng, A (2005, January 20\u201326). Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data. Los Alamitos, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lim, E., and Suter, D (2007, January 24\u201326). Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction. Hannover, Germany.","DOI":"10.1109\/CW.2007.30"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Munoz, D., Vandapel, N., and Hebert, M (2009, January 12\u201317). Onboard Contextual Classification of 3-D Point Clouds with Learned High-Order Markov Random Fields. Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152856"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1109\/TGRS.2009.2017738","article-title":"A hybrid conditional random field for estimating the underlying ground surface from airborne LiDAR data","volume":"47","author":"Lu","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","first-page":"177","article-title":"The utilisation of airborne laser scanning for mapping","volume":"6","author":"Vosselman","year":"2005","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4525","DOI":"10.3390\/s90604525","article-title":"Building facade reconstruction by fusing terrestrial laser points and images","volume":"9","author":"Pu","year":"2009","journal-title":"Sensors"},{"key":"ref_19","unstructured":"Hadjiliadis, O., and Stamos, I (2010, January 17\u201320). Sequential Classification in Point Clouds of Urban Scenes. Paris, France."},{"key":"ref_20","unstructured":"Lim, E.H., and Suter, D (2008, January 23\u201328). Multi-scale Conditional Random Fields for Over-Segmented Irregular 3D Point Clouds Classification. Anchorage, AK, USA."},{"key":"ref_21","unstructured":"Lam, J., Kusevic, K., Mrstik, P., Harrap, R., and Greenspan, M (2010, January 17\u201320). Urban Scene Extraction from Mobile Ground Based LiDAR Data. Paris, France."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Douillard, B., Brooks, A., and Ramos, F (2009, January 7\u201310). A 3D Laser and Vision Based Classifier. Melbourne, Australia.","DOI":"10.1109\/ISSNIP.2009.5416828"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Halma, A., ter Haar, F., Bovenkamp, E., Eendebak, P., and van Eekeren, A. (2010, January 2). Single Spin Image-ICP Matching for Efficient 3D Object Recognition. Norrk\u00f6ping, Sweden.","DOI":"10.1145\/1877808.1877814"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rusu, R., Bradski, G., Thibaux, R., and Hsu, J (2010, January 18\u201322). Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram. Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651280"},{"key":"ref_25","unstructured":"Johnson, A (1997). Spin-Images: A Representation for 3-D Surface Matching. Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA."},{"key":"ref_26","unstructured":"Kazhdan, M., Funkhouser, T., and Rusinkiewicz, S (2003, January 29\u201331). Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors. San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sun, J., Ovsjanikov, M., and Guibas, L (2009, January 15\u201317). A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion. Berlin, Germany.","DOI":"10.1111\/j.1467-8659.2009.01515.x"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1145\/571647.571648","article-title":"Shape distributions","volume":"21","author":"Osada","year":"2002","journal-title":"ACM Trans. Graph"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Knopp, J., Prasad, M., and Gool, L.V. (2010, January 2). Orientation Invariant 3D Object Classification Using Hough Transform Based Methods. Norrk\u00f6ping, Sweden.","DOI":"10.1145\/1877808.1877813"},{"key":"ref_30","first-page":"553","article-title":"Object Detection from Large-Scale 3D Datasets Using Bottom-Up and Top-Down Descriptors","volume":"5305","author":"Forsyth","year":"2008","journal-title":"ECCV (4)"},{"key":"ref_31","unstructured":"Liu, Y., Zha, H., and Qin, H (2006, January 17\u201322). Shape Topics-A Compact Representation and New Algorithms for 3D Partial Shape Retrieval. New York, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Klasing, K., Althoff, D., Wollherr, D., and Buss, M (2009, January 12\u201317). Comparison of Surface Normal Estimation Methods for Range Sensing Applications. Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152493"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.cagd.2005.03.006","article-title":"Surface mesh segmentation and smooth surface extraction through region growing","volume":"22","author":"Vieira","year":"2005","journal-title":"Comput. Aided Geom. Des"},{"key":"ref_34","unstructured":"Wang, J (2008). Graph Based Image Segmentation: A Modern Approach, VDM Verlag Dr. M\u00fcller Aktiengesellschaft & Co."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., and Frenkel, A (2011, January 9\u201313). On the Segmentation of 3D LIDAR Point Clouds. Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979818"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Friedman, S., and Stamos, I (2011, January 16\u201319). Real Time Detection of Repeated Structures in Point Clouds of Urban Scenes. Hangzhou, China.","DOI":"10.1109\/3DIMPVT.2011.35"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.isprsjprs.2007.07.005","article-title":"Geometric validation of a ground-based mobile laser scanning system","volume":"63","author":"Barber","year":"2008","journal-title":"ISPRS J. Photogramm"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fung, B., Wang, K., and Ester, M (2003, January 1\u20133). Hierarchical Document Clustering Using Frequent Itemsets. San Francisco, CA, USA.","DOI":"10.1137\/1.9781611972733.6"},{"key":"ref_39","unstructured":"Rosenberg, A., and Hirschberg, J (2007, January 28\u201330). V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. Prague, Czech Republic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/4\/1624\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:45:52Z","timestamp":1760219152000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/4\/1624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,3,28]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2013,4]]}},"alternative-id":["rs5041624"],"URL":"https:\/\/doi.org\/10.3390\/rs5041624","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,3,28]]}}}