Wavelet transform of Gaussian progressive decomposition method for full-waveform LiDAR data
Received:April 14, 2017  Revised:September 14, 2017  download
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Author NameAffiliationE-mail
YANG Xue-Bo Key Laboratory of Digital Earth ScienceInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences yangxb@radi.ac.cn 
WANG Cheng Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences wangcheng@radi.ac.cn 
XI Xiao-Huan Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences xhxi@ceode.ac.cn 
TIAN Jian-Lin Sun Yat-sen University 185363236@qq.com 
NIE Sheng Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences niesheng@radi.ac.cn 
ZHU Xiao-Xiao Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences zhuxx@radi.ac.cn 
Abstract:Gaussian decomposition is the most commonly used methods for waveform analysis, which is a key post-processing step for the applications of space-borne LiDAR data. However, it usually fails to detect the overlapping pulses of large-footprint waveform data. Therefore, a Gaussian progressive decomposition method based on wavelet transform was proposed in this study to address this issue and applied to Ice, Cloud, and land Elevation Satellite / Geoscience Laser Altimeter System (ICESat/GLAS) data. The new proposed method mainly consists of three key steps. First, the wavelet transform was adopted to detect the target features and estimate the component feature parameters, then the Gaussian model was established to optimize the feature parameters. Second, a new component was added if the fitting accuracy didn’t meet the requirements. Finally, waveform decomposition based on wavelet transform was completed until no more new components were added. Additionally, a comparison experiment between the new proposed method and the Gaussian decomposition method based on inflection point was also conducted to verify the reliability of the new proposed algorithm. Experiment results indicated that our new proposed algorithm can detect twice targets as many as the method based on inflection point, and effectively decompose the targets from overlapping waveforms due to high fitting accuracy of above 98%.
keywords:Large footprint LiDAR  full-waveform analysis  wavelet transform  Gaussian decomposition  characteristic parameter
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Copyright:《Journal of Infrared And Millimeter Waves》