Farhad Samadzadegan 
  
Altogether currently 10 fuzzy rules are formulated and used for constraining matching. For all these fuzzy if-then rules 
of matching the linguistic output is "corresponding point". Taking all membership functions into account fuzzy 
inference will lead to the membership function of corresponding points plotted in Figure 10. 
  
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Figure 10: Membership function of corresponding points 
After defuzzification the resulting matched point probabilities are determined. All corresponding points with 
probabilities higher than 50% may be introduced into further processes. 
4 OBJECT SPACE PROCESS 
Processing in object space basically starts with the co-ordinates of 3D points calculated by spatial forward intersection. 
From the generally huge number of discrete mass points a continuous surface is approximated by FE modelling, 
Continuity and smooth transition between neighbouring finite elements is introduced by regularisation. In the 
implemented procedure a spline based FE model is used. 
To deal properly with outliers the least squares estimation process of estimating the terrain surface is robustified, 
Robust estimation using weight functions and iterative solutions are powerful for big data sets with high redundancy, 
Because the number of matched points for each DTM grid is generally high, robust estimation is quite successful in 
carrying out DTM interpolation. Details on those concepts are eg. given in Hahn, 1989 and Ackermann and Krzystek, 
1991. 
To develop a refinement of this procedure of DTM interpolation with a higher degree of robustness the points should 
possess a certain semantic meaning. For this purpose the key points should be assigned to fuzzy sets containing, for 
example, general topographic points, edge points with 3D discontinuities of different order, points which are likely to 
belong to surfaces of manmade objects or other objects like trees. Rules like those outlined in section 2 have to be 
developed and integrated in fuzzy inference processes to make the information more explicit which is only incompletely 
and implicitly available in the images. The assignment to fuzzy sets without the need to be semantically accurate is 
certainly a benefit for a development based on fuzzy logic. 
5 EXPERIMENTS 
Some first experiments are carried out with the presented 
fuzzy method for automatic DTM generation. 
The data set used is a mountainous area in the North- 
Western part of IRAN. The scale of the photographs is 
1:10.000, the focal length 150 mm and the length overlap 
of two stereo images is about 60%. Thus the terrain area 
imaged in both images covers an area of 1.5km x 3km. 
The images were scanned by 20u pixel size by the high 
precision photogrammetric scanner of Intergraph. Figure 
11 shows an overview image of this area. 
Obviously this is a very meagre, rocky area. But it is a 
typical image for the mountainous regions in IRAN 
showing the challenge for manual DTM data capture as 
well as for automatic procedures. 
Manual measurements are carried out by a very 
experienced operator using the ParadEyes station, which is 
a digital photogrammetric workstation. The contour lines 
Figure 11: Image used in the experiments 
  
804 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 
  
  
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