本文選題：全波形Li　切入點(diǎn)：DAR　出處：《測(cè)繪與空間地理信息》2016年07期 　論文類型：期刊論文

【摘要】：LM(Levenberg-Marquardt)算法是全波形機(jī)載激光雷達(dá)(Li DAR)數(shù)據(jù)高斯分解中求解模型參數(shù)的一種方法。針對(duì)其結(jié)果在一定程度上依賴初值、雅克比矩陣出現(xiàn)非數(shù)值導(dǎo)致無(wú)結(jié)果等問(wèn)題,本文提出分組LM算法,以廣義高斯混合函數(shù)為模型,模型參數(shù)初始化后,將參數(shù)分組并使用LM算法依次對(duì)各組參數(shù)進(jìn)行優(yōu)化,并生成點(diǎn)云。為驗(yàn)證結(jié)果的可靠性,以系統(tǒng)點(diǎn)云為參考,與基于改進(jìn)的EM(Expectation Maximum)算法全波形分解法做對(duì)比。結(jié)果表明,本方法不僅得到較高質(zhì)量的點(diǎn)云,而且得到回波位置和寬度等信息。
[Abstract]:The LMU Levenberg-Marquardt algorithm is a method to solve the model parameters in the decomposition of the data Gao Si of the full waveform airborne lidar / Li DAR. In view of the problem that the results depend on the initial value to some extent, the Jacobian matrix is nonnumerical and has no results. In this paper, a grouping LM algorithm is proposed, in which the generalized Gao Si mixed function is taken as the model. After the model parameters are initialized, the parameters are grouped and optimized by LM algorithm, and point clouds are generated to verify the reliability of the results. The system point cloud is taken as a reference and compared with the full-waveform decomposition method based on the improved EM(Expectation maximum algorithm. The results show that the method not only obtains a high quality point cloud, but also obtains the echo position and width information.
【作者單位】： 武漢大學(xué)遙感信息工程學(xué)院;武漢大學(xué)測(cè)繪遙感信息工程國(guó)家重點(diǎn)實(shí)驗(yàn)室;浙江省第二測(cè)繪院;
【分類號(hào)】：P237

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