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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
Both methods remove a large number of errors. However, a
visual test is still essential and will be done manually. Points
that are obviously not part of the surface will be removed from
the dataset by using an eraser function in a 3D view (see Figure
2 below). Finally, the ground points are matched to a triangular
irregular net (TIN) by a Delaunay triangulation.
The small grid size of 50x50 cm for the selected ground points
is too great in resolution in a homogeneous surface. For this
reason the resulted TIN has more triangles than are necessary.
An automatic reduction of the TIN based on the angle deviation
of neighboured areas therefor follows. A predefined deviation
of the angle decides which trinangles will be merged.
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Figure 2. For extracting a Digital Terrain Model (DTM) a
regular raster 1s used to separate a few ground
points out of the full scan point cloud. The figure
above shows the pre-filtered ground points by using
a regular raster of 30x30m with a grid size of
50x50cm. Below the resulting filtered ground points
are a result of interactive correction.
2.4 Automatic Recognition of Trees
[Important objects in forests are trees with their position and
dimension. For a standard data analysis, an automatic process
for localising trees is essential. In order to achieve this aim a
method has been developed which identifies trees by using a
Hough-transformation (Halcon 2004 ;Pitas, 2000; Simonse,
2003).
In a horizontal layer, cut from the scanned point cloud, a tree
stem is mapped as a circle or a circular arc. Because of the
changing terrain the layers have to be within an isochronous
distance to the terrain in order to cut layers from the trees in the
predefined terrain level. For this the above described DTM is
used.
Even with the filter methods to eliminate incorrect points in the
raw data, the result of a single layer is also influenced by errors
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Figure 3. Found Tree Circles in layers of 10 cm thickness in a
perspective view.
and artefacts. To obtain reliable information of the trees, layers
at different levels are cut. The results of the layers in the
different levels will then be merged to improve the results.
Furthermore, the taper is also relevant and can also be
investigated by using different layers.
The layers are usually 10 cm thick and localised between 1 and
20 m above the ground. The increment of the layers is 0.5 m. Of
special interest to forest management is the diameter at 1.3m
above ground. To properly obtain this value, additional layers at
1.2m and 1.4m are extracted. The diameter at 1.3m will be
verified by the additional diameters at 1.2m and 1.4m.
To use standard pattern recognition methods, the layers are
mapped to a regular raster with a cell size of 1x1 cm. For
detecting circles in this raster image, a Hough-transformation,
which requires a predefined diameter, is used. Because the
diameter is not known before the algorithm is started, we begin
with a diameter value of 100 cm and reduce the diameter in
increments of 10 cm. The results of this are positions and
diameters of a low precision. To improve the position and
diameter a fit circle algorithm is used. The determined Hough
circle is expanded by 10% to ensure the identification of all
pixels that could be part of the stem. On the selected pixels an
algebraic algorithm is used to fit the circle precisely. This
algorithm minimizes the algebraic distance between the contour
points and the resulting circle (Halcon, 2004; Simonse 2003).
Unfortunately not all detected circles represent a tree stem.
Some circles are the result of noisy points or scan points in
shrubs or tree crowns. To eliminate such circles a stepwise
method is used. The first step investigates the position and
dimension of the lower 7m portion of the trees. All detected
circles in this section are put on top of each other with common
intersection areas. The circles which belong to the same
intersection belong together and are circles of the same tree. To
reduce errors, a minimum of 5 circles in one intersection is
necessary to define a tree.
The second step investigates the upper part of the trees above 7
m. In this section branches hardly influenced the recognition.
For this reason, in the upper part only circles are used to try to
find the position of the already detected trees. For this the same
Hough transformation and fit circle algorithm as in the first step
is used. The investigated area, however, is different. Only in the
closer neighbours of the already found trees will the layers be
investigated for additional circles.
As a validity check a linear regression is used. To ensure that a
circle dataset of a tree has no strongly decreasing diameter in
