Multispectral LiDAR Point Cloud Classification: A Two-Step Approach

doi:10.3390/rs9040373

CORC > 武汉物理与数学研究所 > 中国科学院武汉物理与数学研究所 > 高技术创新与发展中心

	Multispectral LiDAR Point Cloud Classification: A Two-Step Approach
	Du, Lin 2,5; Yang, Jian 5; Zhang, Qingjun 3,4; Gong, Wei 4,5; Shi, Shuo 4,5; Chen, Biwu 5; Sun, Jia 5; Zhang, Zhenbing 5; Song, Shalei 1
刊名	REMOTE SENSING
	2017-04-01
卷号	9 期号:4
关键词	Lidar Multispectral Point Cloud Classification K-nearest Neighbors Vegetation Index
DOI	10.3390/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)
内容类型	期刊论文
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).