Abstract
Abstract. Separating point clouds into ground and non-ground points is a necessary step to generate digital terrain model (DTM) from LiDAR dataset. In this research, a new method based on multi-scale analysis of height difference threshold is proposed for ground filtering of LiDAR data. The proposed method utilizes three windows with different sizes in small, average and large to cover the entire LiDAR point clouds, then with a height difference threshold, point clouds can be separated to ground and non-ground in each local window. Meanwhile, the best threshold values for size of windows are considered based on physical characteristics of the ground surface and size of objects. Also, the minimum of height of object in each window selected as height difference threshold. In order to evaluate the performance of the proposed algorithm, two datasets in rural and urban area were applied. The overall accuracy in rural and urban area was 96.06% and 94.88% respectively. These results of the filtering showed that the proposed method can successfully filters non-ground points from LiDAR point clouds despite of the data area.
Highlights
In recent decades, airborne light detection and ranging (LiDAR) has been demonstrated to be useful for rapidly gathering three-dimensional coordinates of ground and nonground objects (Rabbani and Van Den Heuvel 2005; Rabbani et al 2007; Filin and Pfeifer 2006)
We propose a new method based on multi-scale analysis of height difference threshold on raw LiDAR point clouds for filtering of LiDAR data
A new LiDAR point clouds filtering based on multi-scale analysis on raw LiDAR data was proposed
Summary
Airborne light detection and ranging (LiDAR) has been demonstrated to be useful for rapidly gathering three-dimensional coordinates of ground and nonground objects (Rabbani and Van Den Heuvel 2005; Rabbani et al 2007; Filin and Pfeifer 2006). Search for neighboring points by creating a Triangulated Irregular Network (TIN) with certain constraints of angle and distance (Uysal and Polat 2014) This algorithm assumes that bare ground areas are usually smooth surfaces without sharp corners in terrain surfaces (Meng, Currit, and Zhao 2010). Arefi and Hahn (2005) developed a morphological method based on geodesic dilation and changing window sizes to gradually remove non-ground points. Their results show that the selection of window sizes is critical to removing objects with different sizes. The paper explains the basic procedure of this algorithm and presents results and analyses obtained from its implementation
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