{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T14:50:18Z","timestamp":1766587818411,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB0504500"],"award-info":[{"award-number":["2018YFB0504500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971307"],"award-info":[{"award-number":["41971307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017T100582"],"award-info":[{"award-number":["2017T100582"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial\u2013spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial\u2013spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information\u2019s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial\u2013spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data.<\/jats:p>","DOI":"10.3390\/rs13204118","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T23:02:16Z","timestamp":1634252536000},"page":"4118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection"],"prefix":"10.3390","volume":"13","author":[{"given":"Shuo","family":"Shi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0589-0706","authenticated-orcid":false,"given":"Sifu","family":"Bi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China"}]},{"given":"Wei","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China"},{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6361-3005","authenticated-orcid":false,"given":"Biwu","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Radio Equipment Research Institute, Shanghai 201109, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9975-7983","authenticated-orcid":false,"given":"Bowen","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China"},{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}]},{"given":"Xingtao","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China"}]},{"given":"Fangfang","family":"Qu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China"}]},{"given":"Shalei","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"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. Environ."},{"key":"ref_2","unstructured":"Benediktsson, J.A., Chanussot, J., and Fauvel, M. (July, January 29). Multiple classifier systems in remote sensing: From basics to recent developments. Proceedings of the International Workshop on Multiple Classifier Systems, G\u00fcnzburg, Germany."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.1109\/JSTARS.2015.2432037","article-title":"Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning","volume":"8","author":"Khodadadzadeh","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2010.08.007","article-title":"Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests","volume":"66","author":"Guo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.3390\/rs3112364","article-title":"Fusion of high resolution aerial multispectral and LiDAR data: Land cover in the context of urban mosquito habitat","volume":"3","author":"Hartfield","year":"2011","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1016\/j.rse.2009.04.007","article-title":"Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study","volume":"113","author":"Zhou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Luo, S., Wang, C., Xi, X., Zeng, H., Li, D., Xia, S., and Wang, P. (2016). Fusion of airborne discrete-return LiDAR and hyperspectral data for Land cover classification. Remote Sens., 8.","DOI":"10.3390\/rs8010003"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Song, S., Li, P., Gong, W., Ma, Y., and Li, J. (2008, January 28\u201329). Application and key techniques of multi-wavelength lidar. Proceedings of the Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, Guangzhou, China.","DOI":"10.1117\/12.813152"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2012.02.001","article-title":"Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance","volume":"69","author":"Wei","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/LGRS.2011.2113312","article-title":"A multispectral canopy LiDAR demonstrator project","volume":"8","author":"Woodhouse","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4942","DOI":"10.1109\/TGRS.2013.2285942","article-title":"Design and evaluation of multispectral lidar for the recovery of arboreal parameters","volume":"52","author":"Wallace","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/LGRS.2015.2410788","article-title":"Design of a new multispectral waveform LiDAR instrument to monitor vegetation","volume":"12","author":"Niu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wichmann, V., Bremer, M., Lindenberger, J., Rutzinger, M., Georges, C., and Petrini-Monteferri, F. (2015). Evaluating the potential of multispectral airborne LIDAR for topographic mapping and land cover classification. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 2.","DOI":"10.5194\/isprsannals-II-3-W5-113-2015"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Baku\u0142a, K., Kupidura, P., and Je\u0142owicki, \u0141. (2016). Testing of land cover classification from multispectral airborne laser scanning data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 41.","DOI":"10.5194\/isprs-archives-XLI-B7-161-2016"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Morsy, S., Shaker, A., and El-Rabbany, A. (2017). Multispectral LiDAR data for land cover classification of urban areas. Sensors, 17.","DOI":"10.3390\/s17050958"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fernandez-Diaz, J.C., Carter, W.E., Glennie, C., Shrestha, R.L., Pan, Z., Ekhtari, N., Singhania, A., Hauser, D., and Sartori, M. (2016). Capability assessment and performance metrics for the Titan multispectral mapping lidar. Remote Sens., 8.","DOI":"10.3390\/rs8110936"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Teo, T.-A., and Wu, H.-M. (2017). Analysis of land cover classification using multi-wavelength LiDAR system. Appl. Sci., 7.","DOI":"10.3390\/app7070663"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1109\/JSTARS.2018.2835483","article-title":"Classification of airborne multispectral lidar point clouds for land cover mapping","volume":"11","author":"Ekhtari","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.isprsjprs.2017.04.005","article-title":"Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating","volume":"128","author":"Matikainen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Matikainen, L., Karila, K., Hyypp\u00e4, J., Puttonen, E., Litkey, P., and Ahokas, E. (2017). Feasibility of multispectral airborne laser scanning for land cover classification, road mapping and map updating. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42.","DOI":"10.1016\/j.isprsjprs.2017.04.005"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Morsy, S., Shaker, A., El-Rabbany, A., and LaRocque, P.E. (2016). Airborne multispectral LIDAR data for land-cover classification and land\/water mapping using different spectral indexes. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 3.","DOI":"10.5194\/isprsannals-III-3-217-2016"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Morsy, S., Shaker, A., and El-Rabbany, A. (2017). Clustering of multispectral airborne laser scanning data using gaussian decomposition. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42.","DOI":"10.5194\/isprs-archives-XLII-2-W7-269-2017"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huo, L.-Z., Silva, C.A., Klauberg, C., Mohan, M., Zhao, L.-J., Tang, P., and Hudak, A.T. (2018). Supervised spatial classification of multispectral LiDAR data in urban areas. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0206185"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TGRS.2019.2947081","article-title":"A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data","volume":"58","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"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":"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_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Liu, L., Zheng, D., Li, C., and Li, K. (2017). Supervised classification of power lines from airborne LiDAR data in urban areas. Remote Sens., 9.","DOI":"10.3390\/rs9080771"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Thomas, H., Goulette, F., Deschaud, J.-E., Marcotegui, B., and LeGall, Y. (2018, January 5\u20138). Semantic classification of 3D point clouds with multiscale spherical neighborhoods. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00052"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pauly, M., Keiser, R., and Gross, M. (2003, January 1\u20136). Multi-scale feature extraction on point-sampled surfaces. Proceedings of the Computer Graphics Forum, Granada, Spain.","DOI":"10.1111\/1467-8659.00675"},{"key":"ref_32","first-page":"97","article-title":"Dimensionality based scale selection in 3D lidar point clouds","volume":"38","author":"Mallet","year":"2011","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_33","unstructured":"Koller, D., and Sahami, M. (1996). Toward Optimal Feature Selection, Stanford InfoLab."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neucom.2015.05.105","article-title":"Mutual information criterion for feature selection from incomplete data","volume":"168","author":"Qian","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_35","first-page":"3733","article-title":"Research on Feature Selection Algorithm Based on Natural Evolution Strategy","volume":"31","author":"Zhang","year":"2020","journal-title":"J. Softw."},{"key":"ref_36","unstructured":"Holland, J. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press."},{"key":"ref_37","unstructured":"Eberhart, R., and Kennedy, J. (1995, January 4\u20136). A new optimizer using particle swarm theory. Proceedings of the MHS\u201995. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","article-title":"Ant colony optimization","volume":"1","author":"Dorigo","year":"2006","journal-title":"IEEE Computat. Intell. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105190","DOI":"10.1016\/j.knosys.2019.105190","article-title":"Equilibrium optimizer: A novel optimization algorithm","volume":"191","author":"Faramarzi","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Statist."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2053005","DOI":"10.1142\/S0218001420530055","article-title":"Implementation of classification and recognition algorithm for Text information based on support vector machine","volume":"34","author":"Zhang","year":"2020","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7277","DOI":"10.1080\/01431160802326081","article-title":"SVM-based segmentation and classification of remotely sensed data","volume":"29","author":"Lizarazo","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4390","DOI":"10.1016\/j.eswa.2010.09.108","article-title":"Face recognition method based on support vector machine and particle swarm optimization","volume":"38","author":"Wei","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1109\/JSTARS.2013.2272212","article-title":"Urban land cover classification with airborne hyperspectral data: What features to use?","volume":"7","author":"Tong","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7176","DOI":"10.1080\/01431161.2017.1371864","article-title":"A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data","volume":"38","author":"Wu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, X., and Wang, L. (2020). Fine land cover classification in an open pit mining area using optimized support vector machine and worldview-3 imagery. Remote Sens., 12.","DOI":"10.3390\/rs12010082"},{"key":"ref_48","unstructured":"Xiaoliang, Z., Guihua, Z., Jonathan, L., Yuanxi, Y., and Yong, F. (2016). 3D land cover classification based on multispectral lidar point clouds. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 41."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:14:43Z","timestamp":1760166883000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":48,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204118"],"URL":"https:\/\/doi.org\/10.3390\/rs13204118","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}