摘要
点云数据滤波仍旧是现阶段机载LiDAR数据后处理的首要步骤,但其发展尚未完全成熟。在回顾和总结已有滤波算法的基础上,将统计学中偏度与峰度的概念引入到算法中,提出了一种新的基于偏度平衡的地面点与非地面点非监督分类方法,利用统计矩原理从LiDAR点云数据生成的DSM中有效地提取DTM。该方法区别传统算法的最大的优势在于无需参数或者阈值支持,并且相对于LiDAR点云数据的格式和分辨率是独立的。实验结果证明,该方法切实可行,具有较强的适应性,并且能够较好地满足精度要求。
Generally, filtering for point clouds is considered as a primary step for airbome LiDAR data post-processing. However, it is still under development. By reviewing and summarizing the existing filtering algorithms, the concepts of skewness and kurtosis in statistics are introduced to the framework, and a novel unsupervised classification algorithm to differentiate ground and non-ground points based on skewness balancing is presented. Digital Terrain Model（DTM） is effectively extracted from Digital Surface Model （DSM） generated from LiDAR data by exploiting the statistical moments principle. As an alternative to traditional approaches, the ultimate advantages of this algorithm are parameter-and threshold-freedom and independence from LiDAR data format and resolution. Experiment results show that the algorithm is highly practicable and adaptive, and can meet the required precision properly.
出处
《计算机工程与应用》
CSCD
2013年第15期219-223,248,共6页
Computer Engineering and Applications
基金
江西省数字国土重点实验室开放研究基金资助项目(No.DLLJ201111
No.DLLJ201112)
关键词
机载光探测与测量(LiDAR)
滤波
偏度
阈值
非监督分类
airborne Light Detection And Ranging （LiDAR）
filtering
skewness
threshold
unsupervised classification
