Handbook on Advances in Remote Sensing and Geographic Information Systems by Margarita N. Favorskaya & Lakhmi C. Jain

Author:Margarita N. Favorskaya & Lakhmi C. Jain
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

7.3 Densification of LiDAR Point Cloud

The preliminary stage deals with the improvement of the initial LiDAR point cloud concerning to the outliers removal and densification of the LiDAR data. The classification of these methods is situated in Sect. 7.3.1, while the measurement errors are pointed in Sect. 7.3.2.

7.3.1 Densification Approaches

In nearly all the LiDAR applications, the ground filtering is a necessary step to determine, which the LiDAR returned pulses are from the ground surface and which are from the non-ground surfaces [26]. Generally, the main inaccuracy during the DTM generation is caused by the points that have the lowest elevation values than their surrounding neighbors. This is occurs, when a low-lying ground with a small area is measured in a large scale cell. Some approaches can improve this situation, among which are the following ones:The robust linear prediction algorithm proposed by Kraus and Pfeifer [9] involves two steps. First, a rough approximation of a surface is computed using the lowest points. Second, the residuals, (distance vectors from the surface to the measured points) are calculated, and each point receives a weight according to its residual. The points with high weights are attracted the surface whilst the points with low weights have a little influence on a surface structure.

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