Pattern detection in airborne LiDAR data using Laplacian of Gaussian filter |
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| Authors: | Qingming Zhan Yubin Liang Ying Cai Yinghui Xiao |
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| Institution: | School of Urban Design,Wuhan University,8 South Donghu Road,Wuhan 430072,China 2.School of Remote Sensing and Information Engineering,Wuhan University,129 Luoyu Road,Wuhan 430079,China |
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| Abstract: | 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 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 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. |
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| Keywords: | laser scanning point cloud feature detection Laplacian of Gaussian filter |
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