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Multispectral LiDAR Point Cloud Classification: A Two-Step Approach
Chen, Biwu1; Shi, Shuo1,2; Gong, Wei1,2; Zhang, Qingjun2,3; Yang, Jian1; Du, Lin1,4; Sun, Jia1; Zhang, Zhenbing1; Song, Shalei5
2017-04-01
Source PublicationREMOTE SENSING
Volume9Issue:4
SubtypeArticle
AbstractTarget classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50-11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%.
KeywordLidar Multispectral Point Cloud Classification K-nearest Neighbors Vegetation Index
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational Natural Science Foundation of China(41601360 ; National Natural Science Foundation of China(41601360 ; Natural Science Foundation of Hubei Province(2015CFA002) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; 41611130114 ; 41611130114 ; 41571370) ; 41571370) ; National Natural Science Foundation of China(41601360 ; National Natural Science Foundation of China(41601360 ; Natural Science Foundation of Hubei Province(2015CFA002) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; 41611130114 ; 41611130114 ; 41571370) ; 41571370)
DOI10.3390/rs9040373
WOS KeywordLAND-COVER CLASSIFICATION ; SUPPORT VECTOR MACHINE ; WAVE-FORM LIDAR ; HYPERSPECTRAL VEGETATION INDEXES ; AIRBORNE LIDAR ; PADDY RICE ; PRECISION AGRICULTURE ; FLUORESCENCE-SPECTRUM ; CHLOROPHYLL CONTENT ; NITROGEN-CONTENT
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(41601360 ; National Natural Science Foundation of China(41601360 ; Natural Science Foundation of Hubei Province(2015CFA002) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; 41611130114 ; 41611130114 ; 41571370) ; 41571370) ; National Natural Science Foundation of China(41601360 ; National Natural Science Foundation of China(41601360 ; Natural Science Foundation of Hubei Province(2015CFA002) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; 41611130114 ; 41611130114 ; 41571370) ; 41571370)
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000402571700072
Citation statistics
Document Type期刊论文
Identifierhttp://ir.apm.ac.cn/handle/112942/11501
Collection高技术创新与发展中心
Affiliation1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430072, Peoples R China
2.Collaborat Innovat Ctr Geospatial Technol, 129 Luoyu Rd, Wuhan 430072, Peoples R China
3.China Acad Space Technol, Inst Spacecraft Syst Engn, Beijing 100094, Peoples R China
4.Wuhan Univ, Sch Phys & Technol, 129 Luoyu Rd, Wuhan 430072, Peoples R China
5.Chinese Acad Sci, Wuhan Inst Phys & Math, State Key Lab Magnet Resonance & Atom & Mol Phys, 30 Xiao Hongshan Rd, Wuhan 430072, Peoples R China
Recommended Citation
GB/T 7714
Chen, Biwu,Shi, Shuo,Gong, Wei,et al. Multispectral LiDAR Point Cloud Classification: A Two-Step Approach[J]. REMOTE SENSING,2017,9(4).
APA Chen, Biwu.,Shi, Shuo.,Gong, Wei.,Zhang, Qingjun.,Yang, Jian.,...&Song, Shalei.(2017).Multispectral LiDAR Point Cloud Classification: A Two-Step Approach.REMOTE SENSING,9(4).
MLA Chen, Biwu,et al."Multispectral LiDAR Point Cloud Classification: A Two-Step Approach".REMOTE SENSING 9.4(2017).
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