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Proceedings Paper

Research on recognition of healthy situation of vegetation leaves based on multispectral LiDAR
Author(s): Biwu Chen; Shuo Shi; Jia Sun; Wei Gong; Lin Du; Jian Yang; Shalei Song; Binhui Wang
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Paper Abstract

The healthy and withered situation of vegetation has important influence on its biological and chemical process. Neither single wavelength LiDAR (light detection and ranging) nor spectral image can capture the spatial and spectral information of vegetation simultaneously. However, the invention of multispectral LiDAR provided the new method for vegetation detection. There have been some researches on vegetation detection based on multispectral LiDAR, but the potential of multispectral LiDAR’s capability of recognition of healthy and withered vegetation leaves is not totally revealed. So, this research, based on multispectral LiDAR, classified the healthy and withered scindapsus leaves with SVM (support vector machine). And then we also compared the classification capability between the vegetation index and spectral reflectance. The results showed that, the multispectral LiDAR can classify the healthy and withered scindapsus leaves effectively: overall classification accuracy is 95.556%. Compared with spectral reflectance, vegetation index could help increase the classification accuracy: the producer accuracy of withered leaves increased from 23.272% to 70.507%.

Paper Details

Date Published: 15 November 2017
PDF: 8 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106050U (15 November 2017); doi: 10.1117/12.2296282
Show Author Affiliations
Biwu Chen, Wuhan Univ. (China)
Shuo Shi, Wuhan Univ. (China)
Collaborative Innovation Ctr. of Geospatial Technology (China)
Jia Sun, Wuhan Univ. (China)
Wei Gong, Wuhan Univ. (China)
Lin Du, China Univ. of Geosciences (China)
Jian Yang, China Univ. of Geosciences (China)
Shalei Song, Wuhan Institute of Physics and Mathematics (China)
Binhui Wang, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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