{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:56:19Z","timestamp":1766138179270,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["91948303-1","61803375","QL20210018"],"award-info":[{"award-number":["91948303-1","61803375","QL20210018"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["91948303-1","61803375","QL20210018"],"award-info":[{"award-number":["91948303-1","61803375","QL20210018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art.<\/jats:p>","DOI":"10.3390\/rs15010243","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:00:59Z","timestamp":1672628459000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Kai","family":"Xiao","sequence":"first","affiliation":[{"name":"State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Jia","family":"Qian","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5175-0819","authenticated-orcid":false,"given":"Teng","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute for Advanced Study, National University of Defense Technology, Beijing 100020, China"},{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Yuanxi","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shi, S., Bi, S., Gong, W., Chen, B., Chen, B., Tang, X., Qu, F., and Song, S. (2021). Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection. Remote Sens., 13.","DOI":"10.3390\/rs13204118"},{"key":"ref_2","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_3","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_4","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_5","first-page":"1","article-title":"Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance. ISPRS J. Photogramm","volume":"69","author":"Wei","year":"2012","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ibrahim, M., Akhtar, N., Ullah, K., and Mian, A. (2021). Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor. Remote Sens., 13.","DOI":"10.3390\/rs13183621"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, T., Tang, X., Lei, X., and Peng, Y. (2022). Introducing Improved Transformer to Land Cover Classification Using Multispectral LiDAR Point Clouds. Remote Sens., 14.","DOI":"10.3390\/rs14153808"},{"key":"ref_8","first-page":"240","article-title":"Surface drainage features identification using LiDAR DEM smoothing in agriculture area: A study case of Kebumen Regency, Indonesia","volume":"6","author":"Handayani","year":"2022","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/19479832.2022.2047801","article-title":"A segment-based filtering method for mobile laser scanning point cloud","volume":"13","author":"Lin","year":"2022","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103927","DOI":"10.1016\/j.landurbplan.2020.103927","article-title":"Assessing the thermal contributions of urban land cover types","volume":"204","author":"Zhao","year":"2020","journal-title":"Landsc. Urban Plan."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"113","DOI":"10.5194\/isprsannals-II-3-W5-113-2015","article-title":"Evaluating the potential of multispectral airborne LIDAR for topographic mapping and land cover classification","volume":"2","author":"Wichmann","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","first-page":"161","article-title":"Testing of land cover classification from multispectral airborne laser scanning data","volume":"41","author":"Kupidura","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.isprsjprs.2020.03.016","article-title":"A geometry-attentional network for ALS point cloud classification","volume":"164","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"1503","article-title":"Methods from information extraction from lidar intensity data and multispectral lidar technology","volume":"42","author":"Scaioni","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More diverse means better: Multimodal deep learning meets remote-sensing imagery classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., and Felsberg, M. (2017, January 22\u201324). Deep Projective 3D Semantic Segmentation. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden.","DOI":"10.1007\/978-3-319-64689-3_8"},{"key":"ref_19","first-page":"17","article-title":"Unstructured point cloud semantic labeling using deep segmentation networks","volume":"3","author":"Boulch","year":"2017","journal-title":"in3DOR"},{"key":"ref_20","first-page":"1887","article-title":"SqueezeSeg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3D lidar point cloud","volume":"25","author":"Wu","year":"2018","journal-title":"ICRA"},{"key":"ref_21","first-page":"4376","article-title":"SqueezeSegV2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud","volume":"39","author":"Wu","year":"2019","journal-title":"ICRA"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., and Stachniss, C. (2019, January 4\u20138). RangeNet++: Fast and Accurate Lidar Semantic Segmentation. Proceedings of the IROS, Macau, China.","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Meng, H.Y., Gao, L., Lai, Y.K., and Manocha, D. (2019, January 29). VV-Net: Voxelvae Net with Group Convolutions for Point Cloud Segmentation. Proceedings of the ICCV, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00859"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rethage, D., Wald, J., Sturm, J., Navab, N., and Tombari, F. (2018, January 8\u201314). Fully-Convolutional Point Networks for Large-Scale Point Clouds. Proceedings of the ECCV, Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0_37"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dai, A., and Nie\u00dfner, M. (2018, January 8\u201314). 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. Proceedings of the ECCV, Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_28"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jaritz, M., Gu, J., and Su, H. (2019, January 29). Multi-View pointNet for 3D Scene Understanding. Proceedings of the ICCVW, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00494"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep Learning for 3D Point Clouds: A Survey","volume":"23","author":"Guo","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MGRS.2019.2937630","article-title":"Linking Points with Labels in 3D: A Review of Point Cloud Semantic Segmentation","volume":"8","author":"Xie","year":"2020","journal-title":"Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep Learning on Point Sets for 3d Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_30","first-page":"4","article-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"Qi","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, G., Xu, Y., Pan, P., and Xing, Y. (2021). PointNet++ Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud Classification. Remote Sens., 13.","DOI":"10.3390\/rs13030472"},{"key":"ref_32","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., and Guibas, L.J. (2019, January 29). KPConv: Flexible and Deformable Convolution for Point Clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lu, H., Chen, X., Zhang, G., Zhou, Q., Ma, Y., and Zhao, Y. (2019, January 12\u201317). Scanet: Spatial-Channel Attention Network for 3D Object Detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682746"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jing, Z., Guan, H., Zhao, P., Li, D., Yu, Y., Zang, Y., Wang, H., and Li, J. (2021). Multispectral LiDAR Point Cloud Classification Using SE-PointNet++. Remote Sens., 13.","DOI":"10.3390\/rs13132516"},{"key":"ref_37","first-page":"1674","article-title":"On Learning the Right Attention Point for Feature Enhancement","volume":"7","author":"Lin","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"21544","DOI":"10.1364\/OE.451811","article-title":"In-motion continuous point cloud measurement based on bundle adjustment fused with motion information of triple line-scan images","volume":"30","author":"Liao","year":"2022","journal-title":"Opt. Express"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"24043","DOI":"10.1364\/OE.27.024043","article-title":"Hyperspectral lidar point cloud segmentation based on geometric and spectral information","volume":"27","author":"Chen","year":"2019","journal-title":"Opt. Express"},{"key":"ref_40","first-page":"560","article-title":"Fast segmentation of 3D point clouds for ground vehicles","volume":"11","author":"Himmelsbach","year":"2010","journal-title":"IEEE Intell. Veh. Symp."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TPAMI.2004.1261097","article-title":"Two-dimensional PCA: A new approach to appearance-based face representation and recognition","volume":"26","author":"Yang","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.patcog.2005.11.008","article-title":"Rapid and brief communication: Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach","volume":"39","author":"Nagabhushan","year":"2006","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.neucom.2005.06.004","article-title":"Letters: (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition","volume":"69","author":"Zhang","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_44","first-page":"125","article-title":"Saliency detection via two-directional 2DPCA analysis of image patches","volume":"1","author":"Zhang","year":"2014","journal-title":"J. Light Electronoptic"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/S0031-3203(98)00119-8","article-title":"Theoretical Analysis of Illumination in PCA-Based Vision Systems","volume":"32","author":"Zhao","year":"1999","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Turpin, A., and Scholer, F. (2006, January 6\u201311). User Performance Versus Precision Measures for Simple Search Tasks. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA.","DOI":"10.1145\/1148170.1148176"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1055\/s-2007-959046","article-title":"The kappa coefficient","volume":"132","author":"Grouven","year":"2007","journal-title":"Dtsch. Med. Wochenschr."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/19479832.2015.1051138","article-title":"Exploring GIS knowledge to improve building extraction and change detection from VHR imagery in urban areas","volume":"7","author":"Guo","year":"2015","journal-title":"Int. J. Image Data Fusion"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:22Z","timestamp":1760147362000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,31]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010243"],"URL":"https:\/\/doi.org\/10.3390\/rs15010243","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,31]]}}}