摘要
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,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 characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed.
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology. However, 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 characteristics of Laplacian of Gaussian （LOG） Filter and its potential use for structure detection in LiDAR data. A feature detection method based on LoG filtering is presented and ex- perimented on the unstructured points. The method filters the elevation value （namely, z coordinate value） of each point by convo- lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob- jects/features. The experiments are carried on a point cloud dataset acquired from a neighborhood area. The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size, which specifies the local area that is analyzed.
基金
Supported by the National Natural Science Foundation of China (No.40871211)
