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Open AccessArticle

Multispectral LiDAR Point Cloud Classification: A Two-Step Approach

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430072, China
Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China
School of Physics and Technology, Wuhan University, 129 Luoyu Road, Wuhan 430072, China
State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, 30 Xiao Hongshan Road, Wuhan 430072, China
Authors to whom correspondence should be addressed.
Academic Editors: Jie Shan, Guoqing Zhou and Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 373;
Received: 20 September 2016 / Revised: 7 April 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
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%. View Full-Text
Keywords: LiDAR; multispectral; point cloud classification; k-nearest neighbors; vegetation index LiDAR; multispectral; point cloud classification; k-nearest neighbors; vegetation index
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MDPI and ACS Style

Chen, B.; Shi, S.; Gong, W.; Zhang, Q.; Yang, J.; Du, L.; Sun, J.; Zhang, Z.; Song, S. Multispectral LiDAR Point Cloud Classification: A Two-Step Approach. Remote Sens. 2017, 9, 373.

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