A practical method for employing multi-spectral LiDAR intensities in points cloud classification. Issue 21 (1st November 2020)

• Record Type: Journal Article
• Authors: Jiang, Changhui; Chen, Yuwei; Tian, Wenxin; Wu, Haohao; Li, Wei; Zhou, Hui; Shao, Hui; Song, Shaojing; Puttonen, Eetu; Hyyppä, Juha
• Is Part Of: International journal of remote sensing. Volume 41:Issue 21(2020)
• Page Start: 8366
• Page End: 8379
• Publication Date: 2020-11-01
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1775323
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Light Detection and Ranging (LiDAR) intensity is associated with the target surface material, which could help the points cloud classification. However, the intensity is also associated with the laser beam incident angle and the transmitting distance, which obstructs its further application in points cloud classification. Motivated by this problem, this paper proposed a practical method for employing the LiDAR intensities in points cloud classification without distance and incident angle calibration, specifically, ratio values between different spectral channels from a newly invented Hyper-spectral LiDAR (HSL) were defined and calculated for generating robust spectral features. Since the HSL different channels had the same transmitting distance and incident angle, therefore, the ratio values were independent on the laser pulse transmitting distance and laser beam incident angle. An indoor experiment was conducted for fully assessing the proposed method. The HSL had eight different spectral channels with spectral wavelength covering from 650 nm to 1000 nm. In the experiments, papers with different colours were pasted on a flat glass; the HSL scanned them at four distinctive positions with 60 cm displacement. The spectral ratio values between different channels at each position were calculated using the obtained multiple spectral profiles from the HSL. The results showed that the points cloud scanned at different incident and distance could be classified though the spectral ratio values without complex distance and incident angle calibration.