A 
——— A Be 
—— P se ten 
the contour interval 20m. For gross error detection 
different errors had been introduced (single point error, 
pieces of contour lines with wrong height attribute). 
With the utility for quality control and error detection 
errors greater than the contour interval were located. 
Figure 5 shows the SDVs greater than 3*QEV along a 
piece of a contour line with a simulated error of 40m. 
Thereafter the data set was processed for automatic 
derivation of skeleton lines. The algorithm supplied the 
skeleton lines represented in figure 6. Finally the tool for 
generating artificial reference points was applied. 
During the different preparation steps, the quality 
estimation value (QEV) improved from 2.1m to 1.6m. 
The proposed grid width for DTM interpolation was 
25m. Figure 6 shows the example after preprocessing 
andit can be considered as optimal prepared for further 
processing using the program package HIFI. 
As a second example a tacheometric data set was tested. 
The point distribution was very inhomogeneous, 
because many planimetric elements have been measured 
and included into the data set. On the other side the real 
reference points describing the surface in the open field 
are rather sparse. The data set also contains a lot of 
breaklines and border lines of ponds. 
The following results have been obtained from the 
analysis and preparation of the primary data : 
Quality estimation value (QEV) :0. 5m 
threshold value for gross error detection : 2.0 m 
number of artificial reference points : 2843 
proposed DTM grid width :12m 
Figure 7 shows a section of the primary data after 
preprocessing and figure 8 represents the final contours 
derived from the DTM generated with HIFI. 
7. CONCLUSION 
Preparation of primary data for final DTM generation 
by the Finite Element Method needs a number of tools 
as shown above. The use of a preliminary DTM with a 
data structure that can be updated easily enables the 
integration of all these tools into one program 
environment. Such a toolbox, based on the capabilities 
of the GIS Interface of HIFI for data structuring, data 
update and follow-up product derivation, was realized 
and tested at the chair of photogrammetry and remote 
sensing. Advantages of this integrated approach are 
856 
efficient controlling mechanism after all preprocessing 
steps, interactive manipulation by the user and real time 
update of primary data and DTM structure. 
REFERENCES 
Aumann G., Ebner, H., Tang, L., 1991: Automatic deri- 
vation of skeleton lines from digitized contours. 
ISPRS Journal of Photogrammetry and Remote Sen- 
sing, 46, 259-268. 
Aumann G., Ebner, H., 1992: Generation of high fidelity 
digital terrain models from contours. In preparation 
for XVII ISPRS Congress, Washington. 
Ebner, H., Reinhardt, W., Hóssler, R., 1988:Generation, 
management and utilization of high fidelity digital 
terrain models. In: Int. Arch. Photogramm. Remote 
Sensing, Vol. 27, Part B11, pp. 556-566. 
Ebner, H., Hóssler, R., Würlánder, R., 1990: Integration 
of an Efficient DTM Program Package into Geogra- 
phical Information Systems. In: Int. Arch. Photo- 
gramm. Remote Sensing, Vol. 28, Part 4, pp. 116-121. 
Heipke, Ch., 1990: Integration von Bildzuordnung, 
Punktbestimmung, Oberflächenrekonstruktion und 
Orthoprojection innerhalb der digitalen Photogram- 
metrie. Deutsche Geodätische Kommission, Reihe 
C, Heft 366. 
Kóstli, A., Sigle, M., 1986: The random access data 
structure of the DTM program SCOP. In: Int. Arch. 
Photogramm. Remote Sensing, Vol. 26, Part 4, pp. 
45-52. 
Markarovic, B., 1973: Progressive Sampling for Digital 
Terrain Models, ITC Journal, pp. 397-416. 
Reinhardt, W., 1991: Interaktiver Aufbau hochqualitati- 
ver digitaler Gelándemodelle an photogrammetri- 
schen Stereosystemen. Deutsche Geodätische Kom- 
mission, Reihe C, Heft 381.