|تعداد مشاهده مقاله||111,487,330|
|تعداد دریافت فایل اصل مقاله||86,125,542|
Extraction of ground points from LiDAR data based on slope and progressive window thresholding (SPWT)
|Earth Observation and Geomatics Engineering|
|مقاله 4، دوره 2، شماره 1، شهریور 2018، صفحه 36-44 اصل مقاله (991.83 K)|
|نوع مقاله: Original Article|
|شناسه دیجیتال (DOI): 10.22059/eoge.2018.240284.1012|
|Pejman Rashidi1؛ Heidar Rastiveis* 2|
|1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran,|
|2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran|
|Filtering of airborne LiDAR point clouds has broad applications, such as Digital Terrain Model (DTM)|
generation and three-dimensional urban modeling. Although several methods have been developed to
separate the point clouds into ground and non-ground points, there are some challenges to identify the
complex objects such as bridge and eccentric roofs. In this study, a new algorithm based on the Slope and
Progressive Window Thresholding (SPWT) is proposed for ground filtering of LiDAR data. This algorithm
is based on both multi-scale and slope methods that have strong effects on filtering the LiDAR data. The
proposed algorithm utilizes the slope between adjacent points and the elevation information of points in a
local window to detect non-ground objects. Therefore, not only it benefits from vertical information in each
local window to detect the non-ground points, but it also uses the neighbor information in directional
scanning, and it prevents the errors introduced by the sensitivity to direction. According to the physical
characteristics of the ground surface and the size of objects, the best threshold values are considered. In
order to evaluate the performance of the SPWT method, both low and high resolution datasets were applied
that their average overall accuracy were reported to be 94.21% and 93.08%, respectively. These results
proved that, irrespective of data resolution, the SPWT method could effectively remove the non-ground
points from airborne LiDAR data.
|Ground Filtering؛ LiDAR؛ Points Cloud؛ DEM Generation|
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