文献类型：期刊文献

英文题名：A Robust Deconvolution Method of Airborne LiDAR Waveforms for Dense Point Clouds Generation in Forest

作者：Liu, Chang[1] Xu, Lijun[1] Si, Lin[2] Li, Xiaolu[1] Li, Duan[1] Huang, Jianbin[1] He, Yuntao[3]

第一作者：Liu, Chang

通信作者：Li, D[1]

机构：[1]Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China;[2]China Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

年份：2022

卷号：60

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：;EI(收录号：20211210103114);Scopus(收录号：2-s2.0-85102647661);WOS:【SCI-EXPANDED(收录号:WOS:000728266600071)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB0504500 and Grant 2017YFD0600404, in part by the National Natural Science Foundation of China (NSFC) under Grant 61901023, in part by the National Defense Key Laboratory of Aerospace Intelligent Control Technology of BICE under Grant 6142208190101, and in part by the School of Electronic and Information Engineering, Beihang University, under Grant 11673079.

语种：英文

外文关键词：Three-dimensional displays; Deconvolution; Laser radar; Forestry; Lasers; Vegetation; Splines (mathematics); Automatic stopping criterion; deconvolution; dense point generation; false subwaveform removal; full waveform light detection and ranging (LiDAR)

摘要：The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9x0025; higher than that of the RL and 20x0025; higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3x0025; and 35x0025; higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22x0025;, 51x0025;, and 57x0025; greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.