Structural and Geometrical Vegetation Filtering - Case Study on Mining Area Point Cloud Acquired by UAV Lidar

products of bulk data collection. Commonly used filtering methods have
been developed for large areas, usually scanned from an aircraft at high
altitude, where the point clouds are very poorly detailed, and the terrain is
essentially flat. Nowadays, point clouds are generated not only by aerial and
ground scanning but mainly by photogrammetry from UAVs and, more
recently, by scanners mounted on UAVs. Various objects are measured,
including anthropogenic objects, rugged areas with large elevations, rocks,
pits, buildings, etc. Therefore, the aim of filtering is no longer to remove
everything from the cloud except the ground surface but to remove
vegetation as such and some unnecessary objects. In this task, the use of
structure filters, which classify points based on the surrounding of each point
in terms of its structure, seems to be advantageous.
Since many different filtering algorithms have been developed and their
behaviour is controlled by the parameters chosen, it is necessary to test
suitable filters and their settings for each type of area.
In this paper, selected freely available filtering methods based on a geometric
approach are tested as a comparison to the CANUPO-based structure filter,
which is the main object.
Testing of ground filtering procedures on real data acquired by the UAV 3D
scanner DJI L1 corresponding to the nature of the mining area was
performed. Test results were evaluated by type I error, type II error, and total
error, where type I error represents incorrectly unremoved points, type II
error represents incorrectly removed points, and total error represents the
sum of type I error and type II error. The tested geometric filters CSF, PMF
and SMRF showed an overall error of about 7.5% in the best case, of which
error type I constitutes a significantly larger part (about 6%) than error type
II (about 2%). In contrast, the tested CANUPO structural filter in basic use
achieved up to 5.2% total error, using a defined probability bound of up to
4.1%. The distribution of errors of type I and type II is almost even here. The
specific probability set here has a relatively small effect on the result, at 0.1%
of the total error.
Some additional insights into the design and use of filters emerged from the
testing. Geometric filters are significantly faster, but CANUPO is
significantly more reliable in terms of removing vegetation as points having
a character of noise. In particular, the maximum radius used and the total
number of filters must be considered when creating a filtering (training)
prescription.