浏览全部资源
扫码关注微信
辽宁工程技术大学 测绘与地理科学学院, 辽宁 阜新 123000
[ "赵泉华(1978-), 女, 河北承德人, 博士, 教授。2001年于河北理工大学获得学士学位, 2004年、2009于辽宁工程技术大学分别获得硕士、博士学位, 主要研究方向为随机几何、空间统计学、模糊集理论等在遥感图像建模、解译及其在海洋环境遥感中的应用。E-mail:zhaoquanhua@lntu.edu.cn" ]
[ "陈为多(1993-), 女, 辽宁阜新人, 硕士研究生, 2016年于辽宁工程技术大学获得学士学位, 主要研究方向为全波LiDAR数据波形分解、全波LiDAR数据在地物提取方面的应用。E-mail:644746512@qq.com" ]
收稿日期:2017-05-15,
录用日期:2017-7-20,
纸质出版日期:2018-01-25
移动端阅览
赵泉华, 陈为多, 王玉, 等. 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.
Quan-hua ZHAO, Wei-duo CHEN, Yu WANG, et al. Full-waveform LiDAR data decomposition based on skew-normal distribution with unknown number of components[J]. Optics and precision engineering, 2018, 26(1): 161-171.
赵泉华, 陈为多, 王玉, 等. 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. DOI: 10.3788/OPE.20182601.0161.
Quan-hua ZHAO, Wei-duo CHEN, Yu WANG, et al. Full-waveform LiDAR data decomposition based on skew-normal distribution with unknown number of components[J]. Optics and precision engineering, 2018, 26(1): 161-171. DOI: 10.3788/OPE.20182601.0161.
针对传统方法难以实现全波激光雷达数据中非对称波形分解的问题,本文提出一种结合可变分量偏正态模型和可逆跳马尔科夫链蒙特卡洛(RJMCMC)算法的波形分解方法。首先,利用能量函数刻画服从偏正态分布的理想波形与实际波形间的差异程度,并用Gibbs分布定义其似然函数;然后,定义理想波形参数模型的先验分布;在贝叶斯定理框架下,建立具有分量可变性的波形分解模型;设计RJMCMC的移动操作,确定偏正态分布中的分量数以及求解模型参数。利用提出算法,对不同波形特征(偏态、正态)的ICESat-GLAS全波激光雷达数据进行可变分量分解实验。实验结果表明:实验波形结果与实际波形数据相关系数达到0.989以上,所提方法不仅能够同时实现对偏态数据和正态数据的拟合,还能更为准确地确定波形分量数。证明了该方法能实现全波激光雷达数据的精确分解,且分解结果与对应地物高程信息相符。
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.
刘志青, 李鹏程, 陈小卫, 等.基于信息向量机的机载激光雷达点云数据分类[J].光学 精密工程, 2016, 24(1):210-219.
LIU ZH Q, LI P CH, CHEN X W, et al.. Classification of airborne LiDAR point cloud data based on information vector machine[J]. Opt. Precision Eng., 2016, 24(1):210-219. (in Chinese)
WAGNER W, ULLRICH A, DUCIC V, et al.. Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner[J]. ISPRS Journal of Photogra mmetry and Remote Sensing, 2006, 60(2):100-112.
赖旭东, 秦楠楠, 韩晓爽, 等.一种迭代的小光斑LiDAR波形分解方法[J].红外与毫米波学报, 2013, 32(4):319-324.
LAI X D, QIN N N, HAN X SH, et al.. Iterative decomposition method for small foot-print LiDAR waveform[J]. Journal of Infrared and Millimeter Waves, 2013, 32(4):319-324. (in Chinese)
周静平, 张爱武, 王书民.机载小光斑全波形LiDAR数据处理及应用[J].测绘通报, 2013(1):10-13.
ZHOU J P, ZHANG A W, WANG SH M. Processing and application of small-footpring full-waveform airborne LiDAR data[J]. Bulletin of Surveying and Mapping, 2013(1):10-13. (in Chinese)
CHAUVE A, MALLET C, BRETAR F, et al. . Processing full-waveform Lidar data: modelling raw signals[C]. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, ISPRS, 2007: 102-107. https: //hal. archives-ouvertes. fr/lirmm-00293129/
刘峰, 谭畅.全波形LiDar数据分解方法的研究[J].中南林业科技大学学报, 2010, 30(8):148-154.
LIU F, TAN CH. Study of decomposition of full waveform airborne laser scanner data[J]. Journal of Central South University of Forestry & Technology, 2010, 30(8):148-154. (in Chinese)
王素元, 马洪超, 王杰栋, 等.基于分组LM算法的全波形LiDAR高斯分解[J].测绘与空间地理信息, 2016, 39(7):144-147.
WANG S Y, MA H CH, WANG J D, et al.. Gaussian decomposition of full-waveform LiDAR based on grouping LM algorithm[J]. Geomatics & Spatial Information Technology, 2016, 39(7):144-147. (in Chinese)
PERSSONÅ, SÖDERMAN U, TÖPEL J, et al. . Visualization and analysis of full-waveform airborne laser scanner data[C]. Workshop on "Laser Scanning 2005", ISPRS, 2005: 103-108. https: //core. ac. uk/display/21810658
马洪超, 李奇.改进的EM模型及其在激光雷达全波形数据分解中的应用[J].遥感学报, 2009, 13(1):35-41.
Ma H CH, LI Q. Modified EM algorithm and its application to the decomposition of laser scanning waveform data[J]. Journal of Remote Sensing, 2009, 13(1):35-41.(in Chinese)
王蕊, 邢艳秋, 孙小添, 等.机载大光斑激光雷达数据估测森林结构参数研究进展[J].遥感信息, 2015, 30(3):3-9, 23.
WANG R, XING Y Q, SUN X T, et al.. Research progress on airborne large footprint LiDAR data in estimation of forest structure parameters[J]. Remote Sensing Information, 2015, 30(3):3-9, 23. (in Chinese)
庞勇. 星载干涉雷达和激光雷达数据森林参数反演[D]. 北京: 中国科学院遥感应用研究所, 2005. http: //www. doc88. com/p-1354326007151. html
PANG Y. Forest Parameters Inversion Using Spaceborne in SAR and Lidar Technology[D]. Beijing: Institute of Remote Sensing Application Chinese Academy of Science, 2005. (in Chinese)
庞勇, 李增元, MICHAEL L, 等.地形对大光斑激光雷达森林回波影响研究[J].林业科学研究, 2007, 20(4):464-468.
PANG Y, LI Z Y, MICHAEL L, et al.. Effects of terrain on the large footprint lidar waveform of forests[J]. Forest Research, 2007, 20(4):464-468. (in Chinese)
李玉, 徐艳, 赵雪梅, 等.利用高斯混合模型的多光谱图像模糊聚类分割[J].光学 精密工程, 2017, 25(2):509-518.
LI Y, XU Y, ZHAO X M, et al.. Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model[J]. Opt. Precision Eng., 2017, 25(2):509-518. (in Chinese)
赵泉华, 李红莹, 李玉.全波形LiDAR数据分解的可变分量高斯混合模型及RJMCMC算法[J].测绘学报, 2015, 44(12):1367-1377.
ZHAO Q H, LI H Y, LI Y. Gaussian mixture model with variable components for full waveform LiDAR data decomposition and RJMCMC algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12):1367-1377. (in Chinese)
0
浏览量
347
下载量
3
CSCD
关联资源
相关文章
相关作者
相关机构