大地测量与地球动力学2012，Vol.32Issue(2)：60-63,4.
山区LIDAR点云数据的阶层次粗差探测与分析
GROSS ERROR DETECTION AND ANALYSIS BY HIERARCHICAL CLASSIFICATION OF MOUNTAINOUS LIDAR DATA
摘要
Abstract
Gross error detection is one of the important data processing steps of mountainous LIDAR point cloud data. Through analysing the features of gross error distribution, original LIDAR point cloud data can be divided into extreme outliers, outlier clusters and isolated points. On this basis, the idea of hierarchical gross error detection of mountainous LIDAR point cloud data is proposed, and an example of experimental data is verified. Experimental results show that the method can effectively remove gross errors from original mountainous LIDAR point cloud data, and, to a certain extent, improving the effect of pre-processing of point cloud.关键词
山区LIDAR/粗差探测/阶层次/粗差簇/孤立点Key words
mountainous LIDAR/ gross error detection/ hierarchical method/ outlier clusters/ isolated points分类
天文与地球科学引用本文复制引用
李芸,杨志强,杨博..山区LIDAR点云数据的阶层次粗差探测与分析[J].大地测量与地球动力学,2012,32(2):60-63,4.基金项目
福建省广义地质基金项目(20100401) （20100401）
