{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:19:43Z","timestamp":1774379983066,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes.<\/jats:p>","DOI":"10.3390\/rs14061516","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T21:48:42Z","timestamp":1647899322000},"page":"1516","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2594-4054","authenticated-orcid":false,"given":"Lingfeng","family":"Liao","sequence":"first","affiliation":[{"name":"School of Architecture and Urban Planning, Research Institute for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-7397","authenticated-orcid":false,"given":"Shengjun","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Research Institute for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianghai","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Research Institute for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Research Institute for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Research Institute for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5610-3223","authenticated-orcid":false,"given":"Yaxin","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renzhong","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Research Institute for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"ref_1","first-page":"2723","article-title":"Safety Distance Diagnosis of Large Scale Transmission Line Corridor Inspection Based on LiDAR Point Cloud Collected With UAV","volume":"41","author":"Chen","year":"2017","journal-title":"Power Syst. 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