Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification

Sensors

Wei Gong²  Jia Sun²  Shuo Shi²  Jian Yang²  Lin Du²  Bo Zhu²  Shalei Song¹ 

[1] 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; E-Mail:;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430072, China; E-Mails:

关键词: LiDAR;    multispectral;    object classification;    support vector machine;   

DOI  :  10.3390/s150921989

来源: mdpi

【 摘 要 】

The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.