International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
Pre-Filter
of Raw
E Data :
— | Recognition of
Creation the
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Automatic
| Trees
TIN Tree :
Models
Figure 1. Data flow for laser scanning in forest applications. Left. The raw data produced by the laser scanner viewed as a range
and intensity image and in a 3D view. Middle. Stepwise separation of the point cloud by using different filters. Right.
Extracted Parameters and Models.
These targets are also used for geo-referencing the scan plots
into the German national co-ordinate system. The targets are
measured with a total station. Each scanned point cloud is
transformed in this tacheometric system by using a 6 parameter
transformation. As targets, we use plain A4 size printed paper.
The maximum distance of a scanner to a target to insure a good
localisation of the target centre is limited to approximately 20m.
Therefore the area outside this distance is extrapolated. The
residual registration errors are strongest in the extrapolated
areas. In particular we find this problem in the upper part of tree
stems and crowns. To minimize this problem we try to install
targets on a higher level above the ground. Because of the
dangerous access, view distances in the vertical direction are
often not within the maximum range of view distances to
targets.
2.2 Pre-Filtering of Raw Data
Because of the ambiguity problem the raw point cloud contains
a large number of incorrect points. Fortunatly, those detected
points which are farther away have a lower intensity than those
which are closer. For this reason most reflected points out of the
ambiguity interval have a low intensity. This difference in
intensity is used as an initial filter by defining a minimum
threshold for the intensity value.
Another reason for incorrect points is the divergence of the
laser beam. Even with the small diameter of 3mm at 1m
distance and a beam divergence of 0.22mrad, the laser beam
will be reflected at different distances at edges. A part of the
beam will reflect in the foreground and the other in the
background. The calculated scan point is somewhere in between
(Staiger, 2003). To eliminate these incorrect points the scan
direction and the direction between neighbouring points are
used. Each scan point will be tested for the angle between the
direction to the scanner and the eight neighbouring pixels. Scan
points with an angle greater than 170? to one neighbour
minimum are filtered out.
Another filter is used to eliminate isolated points. A point is
considered isolated if there is no neighbour pixel in the range
image within a distance of one meter.
2.3 Creating the Digital Terrain Model
To generate a digital terrain model (DTM) from unclassified
point clouds we separated a sub data set out of the whole point
cloud. The sub data set should include only points of the terrain
surface. To obtain these ground points, a horizontal grid with a
regular cell size of 50 x 50 cm is stretched over the sample plot.
In each grid cell the coordinate with the lowest Z-value is
selected and pre-specified as a ground point. However, not all
grid cells contain a scan point on the terrain surface. Due to the
lower coverage of shrubs and shadow behind trees, there are
quite a number of cells without any scan points, or else with
scan points that are not part of the terrain level (see Figure 2
above), This problem depends not only on the forest density.
On the downhill side of a steep plot there are much fewer scan
points than on an uphill side. Furthermore, in a steep stand with
intense variation, the terrain will become obstructed. To filter
points that are not on the terrain, several filters are used.
As an initial filter a maximum value for the z-coordinate is
determined. Points above this limit cannot be a part of the
ground points and will be removed from the list. The predefined
value for the z-maximum is 10m above the z-value of the
scanner position. In extreme steep stands this predefined value
has to increase. In flat stands this value can decrease to improve
the result extreme. Altogether, this filter performs much better
on flat stands than on steep slopes.
Another filter tests against an exclusion cone around the
scanner centre with a a-priori dihedral angle. Scan points inside
the cone are also eliminated from the ground point dataset. This
filter performs well close to the scanner in steep terrain. When
used uphill this filter improves the result. Downhill, however,
this filter improves less the quality of the result.
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