基于激光雷达波形数据的点云生产

The Study of Point-cloud Production Method Based on Waveform Laser Scanner Data

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现在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3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3维坐标。一般的分解方法基于波形的局部最大值和波形的重心,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脉冲检测算法来得到回波脉冲的位置和宽度,并能得到高质量的点云数据,为DSM(Digital Surface Model)和DTM(Digital TerrainModel)生产提供优质数据源。

Data storage capacity and high processing speed available today has made it possible to digitally sample and store the entire reflected waveform of Small-Footprint Airborne LIDAR （light detection and ranging）, instead of only extracting the discrete coordinates which form the so-called point clouds. One of the most im- portant advantages from waveforms data is that it gives the user the chance to extract three-dimensional coordinates by himself in the post-processing. Decomposition return waveform is a key step during analyzing waveform data. Conventional algorithm to decompose is based on maximum and centre of gravity, or simply by using the thresholding method provided by equipment vendor. Both show lack of high accuracy. In this paper, an improved Expectation Maximum （EM） algorithm is adopted to extract peak location and pulse width from raw waveform data, proving it is a reliable and high accurate decomposition algorithm. Moreover, the highquality point-cloud data could be obtained which provides high-quality resources for DSM（Digital Surface Model） and DTM（Digital Terrain Model） production.