摘要
针对城区LiDAR点云特点,提出一种基于知识的三角网渐进滤波方法:①对格网内插后的栅格数据进行面向对象分割;②采用迭代Otsu聚类手段对地面对象与非地面对象自动分离;③针对分类结果构建初始三角网,并自适应调整地面点判据参数,达到提高滤波质量目的。选用ALS50系统真实数据进行滤波试验,并与传统方法滤波结果进行精度评价,评价结果表明基于知识的滤波方法能进一步提高点云滤波质量。
According to the characteristics of urban LiDAR point clouds,a knowledge-based filtering algorithm with adaptive TIN models is pooposed.The main strategies are： ① taking object-oriented segmentation for raster data interpolated regularly;② separating terrain objects from off-terrain objects by using iteration Otsu clustering method;③ constructing the initial TIN form classification results and adjusting the parameters of the ground point criterion adaptively in the aim of improving the filtering quality.An experiment is done with the real data of ALS50 system,the results quality are assessed with traditional algorithm.The result shows that knowledge-based filtering method can further improve the quality of point clouds filtering.
出处
《测绘学报》
EI
CSCD
北大核心
2012年第2期246-251,共6页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(41071293)
关键词
LIDAR
滤波
知识引导
不规则三角网
两类误差
LiDAR
filtering
knowledge-based
triangular irregular networks
two types errors
