{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T04:29:53Z","timestamp":1783744193800,"version":"3.55.0"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,18]],"date-time":"2017-03-18T00:00:00Z","timestamp":1489795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundations of China (NSFC)","award":["41371405"],"award-info":[{"award-number":["41371405"]}]},{"name":"National Natural Science Foundations of China (NSFC)","award":["41671440"],"award-info":[{"award-number":["41671440"]}]},{"name":"the Basic Research Fund of the Chinese Academy of Surveying and Mapping","award":["777161103"],"award-info":[{"award-number":["777161103"]}]},{"name":"the Foundation for Remote Sensing Young Talents by the National Remote Sensing Center of China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.<\/jats:p>","DOI":"10.3390\/rs9030288","type":"journal-article","created":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T11:39:09Z","timestamp":1490009949000},"page":"288","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests"],"prefix":"10.3390","volume":"9","author":[{"given":"Huan","family":"Ni","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangguo","family":"Lin","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, No. 28 Lianhuachixi Road, Beijing 100830, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban land cover classification using airborne lidar data: A review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. 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