The results of classification have to be thinned out. Points are
found at the positions of relative maxima of texture strength in the
point regions. Line pixels are relative maxima of texture strength
in the direction of the gradient of the grey levels (Fuchs, 1995).
Neighbouring line pixels have to be connected to line pixel
streaks by an edge following algorithm. Finally, these streaks are
to be thinned out and approximated by polygons. Both for line
pixels and points, the co-ordinates are estimated with sub-pixel
accuracy. The algorithm was also tested in an engineering
surveying environment and gave promising results (Mischke et
al., 1997).
In the case of break line detection, we are only interested in
extracting lines. However, the sound statistical background of the
algorithm makes it quite applicable for our purposes. As in our
case the grey levels represent the elevation angle of the surface
normals, the first derivatives of the grey levels correspond to the
changing rate of the terrain steepness, i.e. to the curvature of the
terrain. The positions of maximum directed texture thus
correspond to regions of maximum terrain curvature, and the
thinned-out regions (the results of edge extraction) correspond to
break lines in the terrain model.
This approach for extraction of break lines contains a
simplification because we only use the elevation angle of the
surface normal as an input for edge extraction. Actually, the
elevation can be derived by both co-ordinate directions, and a
more sophisticated way of detecting edges has to make use of
both derivatives. For example, the input could be stored as a
digital image containing two bands, each band corresponding to
the first derivative of the terrain in one of the co-ordinate
directions. Geometrically, our simplification means that we can
not detect break lines between flat terrain regions with equal
steepness but different slope directions. Such break lines typically
appear at symmetrical ridges. As we are especially interested in
extracting roads and because with respect to roads there are no
symmetric ridges, our simplification does not influence the results
of edge extraction with respect to our goals.
2.4 Semi-Automatic Extraction by Snakes
Still, some of the detected lines are broken, and separated
segments appear. Snakes can be used for bridge gaps and deriving
longer segments (Kass et al., 1988). They are commonly used as
semi-automatic line extraction tool in digital images. It is the task
of an operator to provide an approximation of the edge to be
extracted by some seed points. Then the snakes try to detect the
exact edge location automatically by minimising an energy
functional. By this energy functional, a balance between internal
forces (enforcing a smooth shape of the curve) and image forces
(pulling them to salient image features such as edges) is reached.
Gaps in the image edges are bridged in a smooth way by
emphasising the internal terms of the energy functional. For the
specific task of extracting parallel road sides, an extension to the
snakes concept, the twin snakes approach can be used (Kerschner,
1998). This method is less sensitive to the approximation of the
position and shape and has some potential for full automation for
this task. First investigations show promising results.
International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3W14, La Jolla, CA, 9-11 Nov. 1999
2.5 Results of Automatic Break Line Extraction
Figure 7 shows the resulting break lines. Compared with the lines
in figure 5 they seem smoothed and connected to longer
segments. This is the merit of the biased sigma filter. The heights
of the extracted break lines have to be derived from the original
laser data.
Figure 7: Edges extracted from the pre-processed slope model.
For an accuracy analysis of the break lines extracted from the
preliminary DTM, we compared them with geodetically measured
break lines. Thereby we found out that the whole terrain model
had a systematic shift of 2 m in y-direction and 1.2 m in x-
direction (figure 8, left). The reason was an insufficient geo-
referencing of the original laser points because of lack of suitable
control features. The extracted road sides compared to the
measured ones could be used to determine the shift. After
correcting the geo-reference of the DTM the extracted break lines
are very close to the manually measured road sides (figure 8,
right). The roads are found with the correct width while the banks
seem to be wider. The reason for this is that the edges of the roads
are much sharper defined than the edges of the banks. Even
during the terrestrial recording it was difficult to determine the
edges of the banks. The discrepancies lie in the range of 1-2 m,
which is smaller than the definition accuracy of these lines in
nature.
u
i
Figure 8: Comparison between terrestrially measured and
automatically detected road edges before and after correcting the
geo-reference.
In the end we derive a new DTM from both the laser point cloud
and the break lines, and a geomorphologically revised digital
terrain model can be obtained.
International
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