| 改进的EM模型及其在激光雷达全波形数据分解中的应用 |
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| 引用本文： | 马洪超.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). |
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| 作者姓名： | 马洪超 |
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| 作者单位： | 武汉大学遥感学院,湖北武汉,430079 |
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| 基金项目： | 国家重点基础研究发展规划(973计划)，国家高技术研究发展计划(863计划) |
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| 摘 要： | 随着数据存储能力和处理速度的提高,小光斑机载激光雷达系统已经可以通过数字化采样来存储整个反射波形,而不仅仅是由系统提取出来的三维坐标(即离散点云).分析波形数据最重要的优点之一是可以在后处理过程中让使用者自己来提取三维坐标.一般的分解方法基于非线性最小二乘的多项式拟合,或者有设备厂商提供的简单阈值法,无法获得高精度的分解结果.本文使用改进的EM脉冲检测算法得到回波脉冲的位置和宽度,证明是一种性能可靠、精度较高的波形分解算法.
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| 关 键 词： | EM算法 波形数字化 波形分析 高斯分解 |
Modified EM algorithm and its application to the decomposition of laser scanning waveform data |
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| MA Hong-chao.Modified EM algorithm and its application to the decomposition of laser scanning waveform data[J].Journal of Remote Sensing,2009,13(1). |
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| Authors: | MA Hong-chao |
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| Institution: | School of remote sensing,Wuhan University,Hubei Wuhan 430079,China |
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| Abstract: | Small footprint airborne LIDAR systems now possesses the capability to sample the whole returned
waveform rather than to extractdiscrete3D coordinate values (discrete pointcloud), thanks to the improvementofdata
storage hardware and data processing speed. Onemerit to analyze waveform data is that the end-user can extract point
cloud by him /herself from the raw waveform data in the post processing, instead of being provided by the LIDAR
system. The first step to analyzewaveform data is to decompose thewaveform into individualcomponents. Conventional
methods for waveform decomposition are usually polynomial fitting by non-linear least square algorithm, or simply
thresholdingwith the threshold value provided by system vendor. Literature has pointed out that it is impossible to get
higher accurate decomposition results by such conventionalmethods. The paper modifies the Expectation Maximum
(EM) algorithm in the contextof laser scanningwaveform decomposition. Experimentswith data from both airborne and
space borneLIDAR systems show the high reliability and accuracy of the proposedmethod forwaveform decomposition. |
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| Keywords: | LIDAR LIDAR EM algorithm full waveform digitizing waveform decomposisiton Gaussian decomposition |
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