lla, CA, 9-11 Nov. 1999 
  
| position of detailed windows. 
esulting DTM, the road sides must 
th a grid width of 20 x 20 cm! is 
eround points by applying a robust 
stribution function in the program 
3999). Figure 1 shows a hill shaded 
al terrain model is used for the 
model which provides the first 
1 grid point, the elevation angle of 
S") is given in that model. This 
a digital image, where the grey- 
of the terrain (figure 2). As no 
ed yet in interpolating the DTM, a 
'oduced with wide transition zones 
as. 
roads in a mountainous, forested 
les perpendicular to the roads. Up 
cted. Beginning at the left side, the 
ide and the bank of the road (only 
road sides the second and the third 
. is at the end of the ditch. Not all 
À 
p 
— v 
  
  
  
b sg 
'erpendicular to roads. 
   
International Archives of Photogrammetry and Hemote Sensing, Vol. 32, Part 3W14, La Jolla, CA, 9-11 Nov. 1999 
  
Figure 4: Two details from the slope model in figure 2. 
four break lines are significant along the entire road, in fact the 
breaks at the road sides are usually stronger than the outer two 
breaks. Figure 4 shows two details from figure 2 with the position 
of the profiles of figure 3. The roads themselves are relatively flat 
and appear as bright strips in the slope image. They are 
surrounded by the road bank and indentation as dark strips. 
As break lines in the terrain model correspond to abrupt changes 
in the surface normals, they can be detected by applying an edge 
extraction algorithm to the first derivative of elevation. 
Unfortunately, standard edge extraction algorithms deliver only 
short segments of break lines (figure 5) or even fail. 
  
  
  
  
  
Figure 5: Edges extracted from the slope model. 
2.1 Edge Enhancement 
For achieving satisfying results the slope image has to be 
prepared by sharpening the edges. For this purpose an operator 
based on the ideas of the biased sigma filter (Lee, 1983) can be 
used. The biased sigma filter is an edge preserving and edge 
enhancing smoothing filter. In the original concept it determines 
the new grey-level value of a pixel (denoted as the central pixel) 
by calculating two measurements m, and m; using some of the 
neighbouring pixels. Those pixels in a square neighbourhood, that 
have a grey-level value in a defined range around the grey-level 
value of the central pixel (e.g. + three times the standard 
deviation © of the image noise), take part in the calculation. The 
187 
Figure 6: Results of the biased sigma filter. 
measurement my is the average value of all such pixels that have a 
grey-level value smaller than the one of the central pixel. 
Analogously, m, is the average value of such pixels having a 
greater grey-level value. Depending on which one is closer to the 
old grey-level value of the central pixel, either m, or m, is 
selected to be the new grey-level value. 
This filter is quite powerful in smoothing while still preserving 
and even enhancing image edges. Unfortunately it often 
introduces artefacts. In our case we use the edge enhancing 
property of this filter for sharpening widely blurred edges. We 
choose a filter extent of 3 by 3 m? (corresponding to 15 by 15 
pixels). The o-range of the filter is ignored so that all pixels in the 
neighbourhood are used to calculate the two average values m, 
and my, 
The effect of the filter can be seen in figure 6 for the images of 
figure 4. The transition zones between flat and steep (between 
bright and dark) can be removed by this filter which strictly 
assigns either the bright or the dark value to each pixel. The 
resulting image is optimally pre-processed for subsequent edge 
extraction. 
The danger in applying such a strong non-linear filter lies in a 
geometric displacement of image edges. In our case we could not 
find any evidence for this suspicion. The extracted break lines fit 
exactly to the ones measured in the field (c.f. section 2.4). 
2.2 Automatic Feature Extraction 
We use an algorithm for simultaneous extraction of point and line 
features based on the Fórstner Operator (Fuchs, 1995). From the 
first derivatives of the grey levels a measure W for local texture 
strength and a measure Q for isotropy of texture can be computed. 
The average squared norm of the grey level gradients in a small 
(e.g. 5 x 5 pixels) neighbourhood can be used for W. By applying 
thresholds W,,, and Q,, to W and Q, each pixel can be classified 
as belonging either to a homogeneous region, to a point region or 
to a region containing a line. As the classification result is 
especially sensitive to the selection of the threshold W,, for 
texture strength, this threshold is selected in dependence on the 
image contents. The selection of Q,, is less critical because Q is 
bound between 0 and 1 (Mischke et al., 1997).