当前位置: X-MOL 学术Int. J. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A practical method for employing multi-spectral LiDAR intensities in points cloud classification
International Journal of Remote Sensing ( IF 3.0 ) Pub Date : 2020-08-26 , DOI: 10.1080/01431161.2020.1775323
Changhui Jiang 1 , Yuwei Chen 1, 2 , Wenxin Tian 2 , Haohao Wu 2 , Wei Li 2 , Hui Zhou 3 , Hui Shao 4 , Shaojing Song 5 , Eetu Puttonen 1 , Juha Hyyppä 1
Affiliation  

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.

中文翻译:

在点云分类中使用多光谱 LiDAR 强度的实用方法

摘要 光探测和测距 (LiDAR) 强度与目标表面材料相关,有助于点云分类。然而,强度也与激光束入射角和传输距离相关,阻碍了其在点云分类中的进一步应用。受此问题的启发,本文提出了一种无需距离和入射角校准即可在点云分类中使用 LiDAR 强度的实用方法,具体而言,定义并计算了来自新发明的高光谱 LiDAR (HSL) 的不同光谱通道之间的比率值用于生成稳健的光谱特征。由于HSL不同通道具有相同的传输距离和入射角,因此,比值与激光脉冲传输距离和激光束入射角无关。进行了室内实验以充分评估所提出的方法。HSL 有八个不同的光谱通道,光谱波长范围从 650 nm 到 1000 nm。在实验中,将不同颜色的纸粘贴在平板玻璃上;HSL 在四个不同的位置以 60 厘米的位移扫描它们。使用从 HSL 获得的多个光谱轮廓计算每个位置不同通道之间的光谱比值。结果表明,不同入射和距离扫描的点云可以通过光谱比值进行分类,无需复杂的距离和入射角校准。进行了室内实验以充分评估所提出的方法。HSL 有八个不同的光谱通道,光谱波长范围从 650 nm 到 1000 nm。在实验中,将不同颜色的纸粘贴在平板玻璃上;HSL 在四个不同的位置以 60 厘米的位移扫描它们。使用从 HSL 获得的多个光谱轮廓计算每个位置不同通道之间的光谱比值。结果表明,不同入射和距离扫描的点云可以通过光谱比值进行分类,无需复杂的距离和入射角校准。进行了室内实验以充分评估所提出的方法。HSL 有八个不同的光谱通道,光谱波长范围从 650 nm 到 1000 nm。在实验中,将不同颜色的纸粘贴在平板玻璃上;HSL 在四个不同的位置以 60 厘米的位移扫描它们。使用从 HSL 获得的多个光谱轮廓计算每个位置不同通道之间的光谱比值。结果表明,不同入射和距离扫描的点云可以通过光谱比值进行分类,无需复杂的距离和入射角校准。不同颜色的纸被贴在平板玻璃上;HSL 在四个不同的位置以 60 厘米的位移扫描它们。使用从 HSL 获得的多个光谱轮廓计算每个位置不同通道之间的光谱比值。结果表明,不同入射和距离扫描的点云可以通过光谱比值进行分类,无需复杂的距离和入射角校准。不同颜色的纸被贴在平板玻璃上;HSL 在四个不同的位置以 60 厘米的位移扫描它们。使用从 HSL 获得的多个光谱轮廓计算每个位置不同通道之间的光谱比值。结果表明,不同入射和距离扫描的点云可以通过光谱比值进行分类,无需复杂的距离和入射角校准。
更新日期:2020-08-26
down
wechat
bug