{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:39:05Z","timestamp":1775183945545,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T00:00:00Z","timestamp":1562716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research &amp; Development Program of China","award":["41771368"],"award-info":[{"award-number":["41771368"]}]},{"name":"Key Laboratory for National Geographic Census and Monitoring,National Administration of Surveying, Mapping and Geoinformation","award":["2018NGCM06"],"award-info":[{"award-number":["2018NGCM06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.<\/jats:p>","DOI":"10.3390\/rs11141636","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T11:56:51Z","timestamp":1562759811000},"page":"1636","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4611-820X","authenticated-orcid":false,"given":"Xudong","family":"Lai","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2938-6539","authenticated-orcid":false,"given":"Jingru","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4900-1066","authenticated-orcid":false,"given":"Yongxu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0799-3311","authenticated-orcid":false,"given":"Mingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China"},{"name":"Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3822","DOI":"10.1109\/TGRS.2016.2528583","article-title":"A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images with Different Spatial Resolutions","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1080\/01431161.2016.1264028","article-title":"Determining Tree Height and Crown Diameter from High-resolution UAV Imagery","volume":"38","author":"Panagiotidis","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9313","DOI":"10.1080\/01431161.2018.1535932","article-title":"Remote sensing of the coastal zone of the European seas","volume":"39","author":"Marullo","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"Road Recognition From Remote Sensing Imagery Using Incremental Learning","volume":"99","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/JSTARS.2016.2587324","article-title":"A New Building Extraction Postprocessing Framework for High-Spatial-Resolution Remote-Sensing Imagery","volume":"10","author":"Huang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/JSTARS.2016.2603184","article-title":"Building Extraction from Remotely Sensed Images by Integrating Saliency Cue","volume":"10","author":"Li","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","first-page":"4733","article-title":"Geodetic Mass Balance Record with Rigorous Uncertainty Estimates Deducedfrom Aerial Photographs and LiDAR Data\u2013Case Study from Drangaj\u00f6kull Icecap, NW Iceland","volume":"9","author":"Belart","year":"2016","journal-title":"Cryosphere"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"H\u00f6fler, V., Wessollek, C., and Karrasch, P. (2016). Knowledge-Based Modelling of Historical Surfaces Using LiDAR Data. Earth Resour. Environ. Remote Sens.\/GIS Appl. VII, 1\u201311.","DOI":"10.1117\/12.2240388"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"649","DOI":"10.2307\/40035883","article-title":"LiDAR for Archaeological Landscape Analysis: A Case Study of Two Eighteenth-Century Maryland Plantation Sites","volume":"71","author":"Harmon","year":"2017","journal-title":"Am. Antiq."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s10586-017-0759-x","article-title":"Geometric primitive extraction from LiDAR-scanned point clouds","volume":"20","author":"Baek","year":"2017","journal-title":"Clust. Comput."},{"key":"ref_12","first-page":"1","article-title":"Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LiDAR Sensor","volume":"99","author":"Rozsa","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Weng, Q., and Zheng, Y. (2017). A Hybrid Approach for Three-Dimensional Building Reconstruction in Indianapolis from LiDAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9040310"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Cheng, L., Chen, Y., Wu, Y., and Li, M. (2016). Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis. Remote Sens., 8.","DOI":"10.3390\/rs8050419"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13761","DOI":"10.1364\/OE.23.013761","article-title":"Synergistic Application of Geometric and Radiometric Features of LiDAR Data for Urban Land Cover Mapping","volume":"23","author":"Qin","year":"2015","journal-title":"Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1080\/01431161.2015.1137647","article-title":"Extracting Buildings from and Regularizing Boundaries in Airborne liDAR Data Using Connected Operators","volume":"37","author":"Zhao","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.infrared.2018.05.021","article-title":"A top-down Strategy for Buildings Extraction from Complex Urban Scenes Using Airborne LiDAR Point Clouds","volume":"92","author":"Huang","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cad.2017.07.005","article-title":"Urban Building Reconstruction from Raw LiDAR Point Data","volume":"93","author":"Yi","year":"2017","journal-title":"Comput.-Aided Des."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Siddiqui, F., Teng, S., Awrangjeb, M., and Lu, G. (2016). A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery. Sensors, 16.","DOI":"10.3390\/s16071110"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6127","DOI":"10.1080\/01431161.2016.1252472","article-title":"The Importance of Data Type, Laser Spot Density and Modelling Method for Vegetation Height Mapping in Continental China","volume":"37","author":"Liu","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.gmod.2018.04.001","article-title":"Terrain Rendering with Unlimited Detail and Resolution","volume":"97","author":"Kang","year":"2018","journal-title":"Graph. Models"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, L., Chen, J., and Li, M. (2015, January 6\u20139). A Novel Way to Organize 3D LiDAR Point Cloud as 2D Depth Map Height Map and Surface Normal Map. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China.","DOI":"10.1109\/ROBIO.2015.7418964"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/JSTARS.2016.2602439","article-title":"Building Detection Using Enhanced HoG\u2013LBP Features and Region Refinement Processes","volume":"10","author":"Konstantinidis","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/TIP.2017.2694224","article-title":"Learning Multilayer Channel Features for Pedestrian Detection","volume":"26","author":"Cao","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.isprsjprs.2017.06.005","article-title":"Automatic Building Extraction from LiDAR Data Fusion of Point and Grid-based Features","volume":"130","author":"Du","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1080\/02827581.2017.1296181","article-title":"Airborne LiDAR-derived Eelevation Data in Terrain Trafficability Mapping","volume":"32","author":"Niemi","year":"2017","journal-title":"Scand. J. For. Res."},{"key":"ref_27","first-page":"1","article-title":"Sports Inspired Computational Intelligence Algorithms for Global Optimization","volume":"12","author":"Alatas","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1007\/s11227-016-1840-6","article-title":"A Cloud-based Monitoring System via Face Recognition Using Gabor and CS-LBP Features","volume":"73","author":"Li","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kaggwa, F., Ngubiri, J., and Tushabe, F. (2017). Combined Feature Level and Score Level Fusion Gabor Filter-Based Multiple Enrollment Fingerprint Recognition. Int. Conf. Signal Process., 159\u2013165.","DOI":"10.1109\/SCOPES.2016.7955721"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TIP.2017.2783621","article-title":"Fast 2D Complex Gabor Filter with Kernel Decomposition","volume":"27","author":"Kim","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","first-page":"4357","article-title":"Gabor Convolutional Networks","volume":"99","author":"Luan","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TPAMI.2018.2807450","article-title":"A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets","volume":"31","author":"Karanam","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/TIP.2016.2529506","article-title":"Graph-Based Compression of Dynamic 3D Point Cloud Sequences","volume":"25","author":"Thanou","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compind.2018.03.021","article-title":"Multiple Facial Image Features-based Recognition for The Automatic Diagnosis of Turner Syndrome","volume":"100","author":"Song","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Meng, F., Wang, X., Shao, F., Wang, D., and Hua, X. (2019). Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method. Electronics, 8.","DOI":"10.3390\/electronics8010105"},{"key":"ref_36","first-page":"3614","article-title":"Fast Descriptors and Correspondence Propagation for Robust Global Point Cloud Registration","volume":"26","author":"Lei","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1109\/TPWRD.2018.2801332","article-title":"Toward Optimal Multiperiod Network Reconfiguration for Increasing the Hosting Capacity of Distribution Networks","volume":"33","author":"Fu","year":"2018","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ins.2016.01.095","article-title":"A Fast and Robust Local Fescriptor for 3D Point Cloud Registration","volume":"346","author":"Yang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s00521-016-2746-1","article-title":"Feasibility of PSO\u2013ANFIS model to Estimate Rock Fragmentation Produced by Mine Blasting","volume":"30","author":"Hasanipanah","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1007\/s00500-016-2106-1","article-title":"A Binary PSO Approach to Mine High-utility Itemsets","volume":"21","author":"Lin","year":"2017","journal-title":"Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2018.12.031","article-title":"A feature selection approach for hyperspectral image based on modified ant lion optimizer","volume":"168","author":"Wang","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s10489-016-0843-6","article-title":"Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems","volume":"46","author":"Phan","year":"2016","journal-title":"Appl. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/8179670","article-title":"A \u201cTuned\u201d Mask Learnt Approach Based on Gravitational Search Algorithm","volume":"2016","author":"Wan","year":"2016","journal-title":"Comput. Intell. Neurosci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1636\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:04:13Z","timestamp":1760187853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,10]]},"references-count":43,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141636"],"URL":"https:\/\/doi.org\/10.3390\/rs11141636","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,10]]}}}