to the question to what extend information has to be 
available to model discontinuities within the object, in 
order to garantee acceptable results. 
The collection of the informations needed might be 
achieved by visual inspection of the image data or by 
use of other sources (information systems, maps etc.). 
The types of functionals as the size of the regions, for 
example, could be determined by some rough knowled- 
ge about the topography. The definition of the region 
borders will be done by some measurements within the 
digital images (polynomials, corner points etc.). The 
same is valuable for the discretization of discontinuities. 
Much more demanding but of greater importance for 
future developments is the attempt to extract these 
informations automatically from the image data availa- 
ble. The realization of this approach would equip the 
algorithm with interpretative capability, what for sure is a 
necessity to attain the required flexibility within fully 
automated procedures. 
One important prerequisite for the success of the pre- 
processing is the existance of some dependencies 
between the distribution of the image densities and the 
geometry of the corresponding surface part. Although 
there won't exist direct relations, this assumption will 
hold to a certain degree. This might be illustrated by the 
effect of illumination with direct solar radiation, which 
determines the distribution of enlighted and shadowed 
areas by the geometric disposition of the surface. Furt- 
her informations like brightness, texture, edges etc. are 
depending on the underlying objects too. This data 
allows to differentiate between objects, what in many 
cases will be equivalent to a discrimination of changes 
within the surface morphology. 
Existing approaches /Besl, Jain 1988; Grimson, 
Pavlidis 1985; Haralick et al 1983/ document that the 
interrelations between density values and surface topo- 
graphy might be useful to approximate the surface or to 
segment the images into homogeneous regions. Furt- 
hermore there are trends towards algorithms performing 
an extraction of complete objects /Fórstner 1991/ which 
likewise could contribute to the evaluation of the requi- 
red informations. 
Once, the additional knowledge is available it will be 
used within the algorithm to control the calculations. This 
might be done by selection of those points within a zone 
of homogeneous morphology which have to be combi- 
ned to a region, or by evaluation of the results with 
respect to the type of surface. 
In addition the knowledge may be used to segment the 
surface elements into different groups. This is useful in 
case of discontinuities, because then the surface 
elements within a region may belong to different sets of 
surface parameters. Similarily the grouping is conve- 
nient to exclude elements from the calculations or to 
weight them down, what is necessary to eliminate or 
reduce the contribution of some surface parts. 
FIRST RESULTS 
The presented conception has not yet been realized 
131 
completely, but at least the principle of the object space 
based correlation is already transformed into a computer 
algorithm. However, this algorithm does not yet have 
tuning or control capabilities as they will be necessary to 
optimize the results. It therefore seems to be likely to 
expect further improvements with increasing complete- 
ness of the algorithm. 
The tests have been done with already used material 
/Boochs 1987/ showing a topographic surface projected 
into a medium scale (1:12000) image pair. The digital 
images have a resolution of 50 pm. For control purposes 
a set of manual height measurements is available. 
The tests are performed within three small object areas, 
each of them exhibiting different morphology (hillside, 
urban, meadow). 
The results are compared with calculations from a diffe- 
rent correlation algorithm (ARCOS), which has been 
proved as valuable for the determination of digital eleva- 
tion models of smooth topographic surfaces. 
Table 1 shows the mean square residuals (m ) from the 
differences between correlation results and manual 
measurements, the mean square residuals after blunder 
detection (m) and the number of blunders (out). 
obj.sp.cor. ARCOS 
m m out m m out 
qr qr qr qr 
meadow 0.27 0.15 1 0.14 0.14 0 
hillside 0.23 0.19 1 0.68 0.53 1 
urban 0.99 0.99 0 2.41 0.71 5 
Table 1: Mean square residuals [m] 
Regarding at the area with the simplest morphology 
(meadow) we find, that the general accuracy (m) is 
equivalent to the known one from ARCOS and seems to 
be acceptable (0.07*/.. h ) for this application. Certainly, 
a blunder appears within the object space based correla- 
tion. However, it will easily be detected and substituted 
when the internal program control is available. 
Considering the results from the hillside, a considerable 
improvement has to be found. This reflects the advanta- 
ges of a higher complexity within the functional descrip- 
tion of the surface. In this case a polynomial of order 4 
has been used. 
Finally, even for the urban area some positive aspects 
may be found. Although m decreases (0.71m->0.99m) 
there arise no more blunders leading to an improvement 
in m . Nevertheless, these results are not satisfying. 
Further enhancements are to be expected, when the 
discontinuities within urban regions will be modelled 
correctly. In the present stage with the use of polyno- 
mials, results can't be better. After all, the use of small 
and fix image windows seems to be valuable, as the 
geometric distortions will decrease, what might be an 
explanation for the disappearence of the blunders. 
CONCLUSION 
The presented conception has to be seen as contributi- 
on towards higher performance and flexibility of automa-