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新一代星载激光雷达卫星ICESat-2将采用多波束微脉冲光子计数技术,并进行高程剖面式的对地观测。由于该点云数据具有背景噪声大、密度低并呈线状分布等特点,传统的点云滤波算法并不适用,研究新的点云滤波算法十分必要。本文以ICESat-2的机载模拟器MABEL数据为例,首先介绍了微脉冲光子计数激光雷达的基本原理和数据特点,并针对高程剖面点云提出基于局部距离统计和最小二乘局部曲线拟合的点云滤波算法;然后,对美国加利福尼亚州Sierras-Forest地区MABEL试验中532 nm通道的光子点云进行滤波处理,并利用识别的地面点插值得到3 m分辨率的线状DEM,进而估算了该区域美国云杉的平均树高;最后,对该滤波算法进行精度评价,并分析了误差来源及其对DEM精度和树高反演精度的影响。结果表明:(1)该算法整体精度达97.6%,能有效剔除绝大部分噪声点且对地形起伏具有较强的自适应能力;(2)误分噪声点影响了滤波过程中局部地形的拟合,而滤波过程中的分类误差将降低DEM和树高反演的精度。
A new generation of satellite-borne lidar satellites ICESat-2 will use multi-beam micropulse photon counting technology, and the elevation profile of the earth observation. Because the point cloud data has the characteristics of large background noise, low density and linear distribution, the traditional point cloud filtering algorithm is not suitable. It is necessary to study the new point cloud filtering algorithm. In this paper, taking ICESat-2 airborne simulator MABEL data as an example, the basic principle and data characteristics of micropulse photon counting LIDAR are introduced firstly. And the local distance statistics and least square local curve fitting Then, the photon point cloud of the 532 nm channel in the MABEL experiment in the Sierras-Forest area, California, USA, is filtered and the linear DEM with a resolution of 3 m is obtained by interpolation of the identified ground points, and then the The average tree height of American spruce in this area. Finally, the accuracy of this filtering algorithm was evaluated and the source of error and its influence on DEM accuracy and tree height inversion accuracy were analyzed. The results show that: (1) The overall accuracy of the algorithm is 97.6%, which can effectively eliminate most noise points and have strong adaptability to the relief; (2) The misclassification noise points affect the local topography in the filtering process Together, the classification error in the filtering process will reduce the accuracy of DEM and tree height inversion.