Journal
SENSORS
Volume 15, Issue 9, Pages 21989-22002Publisher
MDPI
DOI: 10.3390/s150921989
Keywords
LiDAR; multispectral; object classification; support vector machine
Funding
- National Natural Science Foundation of China [41127901]
- Program for Innovative Research Team in University of Ministry of Education of China [IRT1278]
- Chenguang Project of Wuhan [2014070404010229]
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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.
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