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Remote Sens. 2017, 9(4), 373; doi:10.3390/rs9040373

Multispectral LiDAR Point Cloud Classification: A Two-Step Approach

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China
2
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430072, China
3
Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China
4
School of Physics and Technology, Wuhan University, 129 Luoyu Road, Wuhan 430072, China
5
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
Received: 20 September 2016 / Revised: 7 April 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
View Full-Text   |   Download PDF [8909 KB, uploaded 17 April 2017]   |  

Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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