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Multispectral LiDAR Point Cloud Classification: A Two-Step Approach

文献类型：期刊论文

作者Du, Lin2,5; Yang, Jian5; Zhang, Qingjun3,4; Gong, Wei4,5; Shi, Shuo4,5; Chen, Biwu5; Sun, Jia5; Zhang, Zhenbing5; Song, Shalei1
刊名REMOTE SENSING
出版日期2017-04-01
卷号9期号:4
关键词Lidar Multispectral Point Cloud Classification K-nearest Neighbors Vegetation Index
DOI10.3390/rs9040373
文献子类Article
英文摘要Target 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%.
WOS关键词LAND-COVER CLASSIFICATION ; SUPPORT VECTOR MACHINE ; WAVE-FORM LIDAR ; HYPERSPECTRAL VEGETATION INDEXES ; AIRBORNE LIDAR ; PADDY RICE ; PRECISION AGRICULTURE ; FLUORESCENCE-SPECTRUM ; CHLOROPHYLL CONTENT ; NITROGEN-CONTENT
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000402571700072
资助机构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) ; 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)
源URL[http://ir.wipm.ac.cn/handle/112942/11501]  
专题武汉物理与数学研究所_高技术创新与发展中心
作者单位1.Chinese Acad Sci, Wuhan Inst Phys & Math, State Key Lab Magnet Resonance & Atom & Mol Phys, 30 Xiao Hongshan Rd, Wuhan 430072, Peoples R China
2.Wuhan Univ, Sch Phys & Technol, 129 Luoyu Rd, Wuhan 430072, Peoples R China
3.China Acad Space Technol, Inst Spacecraft Syst Engn, Beijing 100094, Peoples R China
4.Collaborat Innovat Ctr Geospatial Technol, 129 Luoyu Rd, Wuhan 430072, Peoples R China
5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Du, Lin,Yang, Jian,Zhang, Qingjun,et al. Multispectral LiDAR Point Cloud Classification: A Two-Step Approach[J]. REMOTE SENSING,2017,9(4).
APA Du, Lin.,Yang, Jian.,Zhang, Qingjun.,Gong, Wei.,Shi, Shuo.,...&Song, Shalei.(2017).Multispectral LiDAR Point Cloud Classification: A Two-Step Approach.REMOTE SENSING,9(4).
MLA Du, Lin,et al."Multispectral LiDAR Point Cloud Classification: A Two-Step Approach".REMOTE SENSING 9.4(2017).

入库方式： OAI收割

来源：武汉物理与数学研究所

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