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。
Small footprint airborne LIDAR systems now possesses the capability to sample the whole returned waveform rather than to extract discrete 3D coordinate values （discrete point cloud）, thanks to the improvement of data storage hardware and data processing speed. One merit 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 analyze waveform data is to decompose the waveform into individual components. Conventional methods for waveform decomposition are usually polynomial fitting by non-linear least square algorithm, or simply thresholding with the threshold value provided by system vendor. Literature has pointed out that it is impossible to get higher accurate decomposition results by such conventional methods. The paper modifies the Expectation Maximum （EM） algorithm in the context of laser scanning waveform decomposition. Experiments with data from both airborne and space borne LIDAR systems show the high reliability and accuracy of the proposed method for waveform decomposition.
出处
《遥感学报》
EI
CSCD
北大核心
2009年第1期35-41,共7页
NATIONAL REMOTE SENSING BULLETIN
基金
National 973 program(contract No.2009CB724007)
the 11th 863 program(contract No.2006AA12Z101)
关键词
LIDAR
EM算法
波形数字化
波形分析
高斯分解
LIDAR, EM algorithm, full waveform digitizing, waveform decomposisiton, Gaussian decomposition
