Case study: object and ground point separation on a river bank using airborne based LIDAR data
2007
In the past decade, airborne based LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public
sectors as a reliable and accurate source for land surveying in environmental, engineering and civil applications. Commonly, the first
task to investigate LIDAR point clouds is to separate ground and object points. Skewness Balancing has been proven to be an efficient
non-parametric unsupervised classification algorithm to address this challenge. Initially developed for moderate terrain, this algorithm
needs to be adapted to handle sloped terrain. This paper addresses the difficulty of object and ground point separation in LIDAR data
in hilly terrain. A case study on a diverse LIDAR data set in terms of data provider, resolution and LIDAR echo has been carried
out. Several sites in urban and rural areas with man-made structure and vegetation in moderate and hilly terrain have been investigated
and three categories have been identified. A deeper investigation on an urban scene with a river bank has been selected to extend the
existing algorithm. The results show that an iterative use of Skewness Balancing is suitable for sloped terrain.
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