PATTERN DETECTION IN AIRBORNE LIDAR DATA USING LAPLACIAN OF GAUSSIAN FILTER
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology. But it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points. The paper analyzes the theory of Laplacian of Gaussian (LoG) Filter and its potential for structure detection in LiDAR data. A feature detection method based on LoG filtering is presented and experimented on the unstructured points. The method filters the elevation value (namely z coordinate value) of each point by convolution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground objects/features. The experiments are carried on a point cloud dataset acquired from a neighbourhood area. The results demonstrate patterns detected at different scales and the relationship between standard deviation which defmes LoG kernel and neighbourhood size which specifies the local area is analyzed.
Laser Scanning Point Cloud Feature Detection Laplacian of Gaussian Filter
Qingming Zhan Yubin Liang Ying Cai Yinghui Xiao
School of Urban Design, Wuhan University, Wuhan 430072, China;Research Center for Digital City, Wuha Research Center for Digital City, Wuhan University, Wuhan 430072, China;School of Remote Sensing and School of Urban Design, Wuhan University, Wuhan 430072, China;Research Center for Digital City, Wuha
国际会议
武汉
英文
268-272
2011-06-26（万方平台首次上网日期，不代表论文的发表时间）