Abstract
Abstract. This paper presents a hierarchical recovery method to generate DTMs from airborne LiDAR point clouds based one an idea of layering. The developed method first registers the last return points, and then layering them. The layering is done by dividing the points into different height layers and assigning layer numbers to each point. The layer numbers are comparing references in later identification process. Then a series of rasterized pyramid levels which consisted of lowest points are generated. Since the outliers have been removed after the layering, the cells in top level are considered to be terrain points and used as reference to identify cells in the following level. After the identification of the second level, an interpolation will occur in the cells which identified as offterrain. And the interpolated level will be used as reference in its following level and the same process is repeated at each level. Once this process of the bottom level finished, the proposed method adjusts the results based on the first return feedback and followed by the final interpolation. As a result, this produces the final DTM. The developed method is data driven, and does not assume a prior knowledge about the scene complexity. The proposed method was tested with the ISPRS WG III/3 LiDAR datasets covering different terrain types and filtering difficulties. The results show that the proposed method can perform well in flat terrain or gentle slope area.
Highlights
Compared to conventional methods such as aerial photogrammetry and field surveys, the generation of Digital Terrain Models (DTM) from airborne LiDAR point clouds is fast and cost-effective over a large area, especially in vegetation covered areas since laser pulses can penetrate some of the canopy
A total of 15 sites were selected to test the performance of our multi-scale terrain filtering algorithm and compare the results with other methods evaluated by ISPRS (Sithole and Vosselman, 2004)
This paper has presented an automatic method called multiscale terrain filtering to generate DTM from last return points of high resolution airborne LiDAR point clouds
Summary
Compared to conventional methods such as aerial photogrammetry and field surveys, the generation of Digital Terrain Models (DTM) from airborne LiDAR point clouds is fast and cost-effective over a large area, especially in vegetation covered areas since laser pulses can penetrate some of the canopy. Developing an automated and robust approach to terrain point identification and DTM generation is challenging. As a preliminary task of DTM generation using airborne LiDAR point cloud data, filtering terrain and offterrain is critical and fundamental to feature extraction and classification (Briese, 2010). The identified terrain points are the input of further interpolation process in many developed algorithms. Filtering is usually very challenging and time consuming because of the algorithms have to processing a large amount of data. An efficient and effective filter algorithm is important for DTM generation
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