Gaussian Inflection Point Selection for LiDAR Hidden Echo Signal Decomposition

High-quality waveform decomposition, as one of the most critical cores of light detection and ranging (LiDAR) data processing, has become increasingly interesting. However, the current Gaussian decomposition method cannot handle the superimposed waveform with only one peak. Thus, this letter propose...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Zhou, Guoqing, Deng, Ronghua, Zhou, Xiang, Long, Shuhua, Li, Weihao, Lin, Gangchao, Li, Xianxing
Format: Article
Language:English
Subjects:
Algorithms
Data analysis
Data models
Data processing
Decomposition
Distance measurement
Echoes
Full-waveform data
Gauss decomposition
Inflection points
IP networks
Laser radar
Lidar
light detection and ranging (LiDAR)
Research and development
Signal quality
Signal resolution
Vegetation
Vegetation mapping
waveform fitting
Waveforms
Width
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Summary:High-quality waveform decomposition, as one of the most critical cores of light detection and ranging (LiDAR) data processing, has become increasingly interesting. However, the current Gaussian decomposition method cannot handle the superimposed waveform with only one peak. Thus, this letter proposes a Gaussian inflection point selection method (GIPS). The method uses the number of inflection points (IPs) near the peak of the echo signal to judge the position of waveform half-width and selects an appropriate waveform half-width to iteratively decompose the echo signal to obtain Gaussian components, which are combined into a Gaussian model. Finally, a global Levenberg-Marquardt least-square algorithm (LM algorithm) is used to optimize the Gaussian model for fitting the echo signal. To verify the accuracy and effectiveness of GIPS, the experiments were conducted using land, vegetation and ice sensor (LVIS) data. The results show that the GIPS method can decompose complex LiDAR echo signals more correctly and efficiently than other methods do with an average R^{2} of 0.9799.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3107438