光学精密工程2018，Vol.26Issue(1)：161-171,11.DOI:10.3788/OPE.20182601.0161
偏正态全波激光雷达数据的可变分量波形分解
Full-waveform LiDAR data decomposition based on skew-normal distribution with unknown number of components
摘要
Abstract
To decompose asymmetric full-waveform LiDAR data with unknown number of components,a full-waveform LiDAR decomposition method was proposed based on skew-normal distribution and reversible-jump Markov Chain Monte Carlo(RJMCMC)algorithm,which can automatically determine the numbers of components.First,the energy function was used to describe the differences between the actual waveform and the ideal waveform that obeyed the skew-normal distribution,and the likelihood function was defined by Gibbs distribution.Second,the parameter models of the ideal waveform were established using the prior distribution.Then the Bayesian paradigm was followed to build the ideal waveform model.Third,an RJMCMC algorithm was designed to determine the numbers of components and decompose the waveform.The proposed algorithm was used to decompose ICESat-GLAS waveform data in various typical regions. Experimental results indicate that the cross-correlation of the true data and the result is up to 98.9%.The proposed method can not only fit the skewed waveform data and normal waveform data,but also more accurately determine the number of components in comparison to other methods.It can realize the accurate decomposition of full-waveform LiDAR data,and the decomposition result is consistent with the corresponding elevation information.关键词
全波激光雷达/波形分解/偏正态分布/RJMCMC算法Key words
full-waveform LiDAR/waveform decomposition/skew-normal distribution/RJMCMC algorithm分类
信息技术与安全科学引用本文复制引用
赵泉华,陈为多,王玉,李玉..偏正态全波激光雷达数据的可变分量波形分解[J].光学精密工程,2018,26(1):161-171,11.基金项目
辽宁省教育厅科学技术研究一般资助项目(No.LNCL009) （No.LNCL009）
国家自然科学基金青年基金资助项目(No. 41301479) （No. 41301479）
