Post Processing LiDAR Point Clouds

The usual form of delivery of the processed LiDAR data is in the form of a point cloud. This takes one of two forms; [link ASCII text files] or binary ​LAS files. The point clouds contain all the LiDAR data as vectors, a list of individual points, rather than as rasterised data.

Removing Noise Points

As part of the standard processing and quality checking of the LiDAR point clouds, ARSF-DAN classify 'noisy' points. This is done both by using automatic algorithms and by viewing the data. The noisy points can be one of the following:

• isolated points
• isolated clusters of points
• water induced noise
• cloud noise
• system noise

When a point has been identified as being 'noise' it is given a classification value of '7'. All other points will probably have classification of '0' (unclassified) or '1' (default). The classification values used follow the ASPRS standard LiDAR point classes.

It is advised that these points be removed before using the data, unless they are of specific use to your studies.

Removing noisy points from ASCII files

ARSF-DAN include a utility for removing these points from the ASCII files called 'pt_cloud_filter', which has both linux and Windows versions.

To use, open the command line interface (in Windows: click start → click run → type ‘cmd’). Then type:

pt_cloud_filter [input_ascii_file] [classification number(s) to be removed] > [output_filename] 

For example:

pt_cloud_filter.exe point_cloud.txt 7 > filtered_point_cloud.txt

will create a file called filtered_point_cloud.txt from the points in point_cloud.txt excluding those with classification 7.

Removing noisy points from LAS files

To remove them from LAS files it is suggested to download the ​LASTools package, specifically the ​las2las tool.

This can be used to remove noise points by typing:

las2las -i point_cloud.txt -o filtered_point_cloud.txt -drop_class 7

which will create a new file called filtered_point_cloud.txt from the points in point_cloud.txt excluding those with classification 7.