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sigma 0 a posteriori of approx. 0.5 pixel and the RMS error for 
the control points was below 1dm for both test areas. 
4.4 Results 
After image orientation, a DSM with a resolution of 10 cm was 
produced automatically with the in-house developed software 
package SAT-PP (Satellite imagery Precise Processing). The 
software was initially developed for the processing of satellite 
images and later adapted such that it was capable to handle 
still-video camera and aerial images. Due to the combination of 
the multi image approach and the combination of different 
matching methods (area, feature and edge matching) (Zhang, 
2005) allowed the generation of a precise, reliable and 
consistent 3D model of the maize field. Finally, an orthophoto 
with a footprint of 3 cm was produced using the generated 
DSM and integrated into a GIS for further analysis (see 
Figure 7). 
Figure 7: Screenshot of the 3D-model of experiment B (2006) 
using the Swissimage orthoimage (swisstopo®) draped over the 
DHM25 (swisstopo®) combined with the orthoimage of the 
maize field and the position of the cross-pollination data. 
5. CONCLUSIONS AND FUTURE WORK 
In this study, a workflow for the processing of UAV data was 
presented. The processing steps of the workflow are based on 
modules, which allow the independent adaptation of the 
individual steps. 
The autonomous flight allowed us to predefine the flight 
trajectory for an optimal photogrammetric processing. The 
flight trajectories for both projects were calculated 
automatically by in-house developed software using existing 
topographic maps, orthophotos and elevation models. For the 
Randa project, the flight planning was modified to the non 
nadir case. Two autonomous flying modes were applied in the 
maize field project: stop modus and cruising modus. By using 
the cruising mode the flight time could be reduced by 75 
percent for the observation of one field. The performance of the 
system during the cruising mode is comparable with fixed-wing 
UAV systems (Horcher and Visser, 2004), while the stabilizer 
system of the helicopter allowed a continuous and stabilized 
flight. In addition, the helicopter system is more flexible than 
comparable fixed wing systems due to the system characteristics. 
Therefore, the used autonomous flying helicopter system can be 
introduced in several applications like crop maps for biomass 
measurement, as input for tractor-automated guidance (Kise et al., 
2005), in forestry, for monitoring and detection and prediction of 
erosion using the extracted image data, orthophotos and elevation 
model. Furthermore, the mini UAV-system can be used for 3D 
city modeling in combination with an autonomous car (Lamon et 
al., 2006). 
In comparison to the mobile stereovision system, proposed by 
Rovira-Mas et al. (2005) and Rovira-Mas et al. (2008), our 
method allows an autonomous documentation with the UAV 
system by producing accurate and reliable elevation models and 
orthoimages of the site. The produced data can be directly 
integrated into several GIS systems using absolute coordinates. 
Therefore, the data can be easily combined with any other 
reference data. Furthermore, by using medium format still-video 
cameras, the image and the derived DSM resolution allows 
analysis at a larger scale as with stereovision systems using the 
same flight height. Therefore, large sites can be documented 
completely in a shorter time period with our method. 
Orientation values were calculated by using the Kalman filter 
implemented in the navigation unit on board the mini UAV as 
initial exterior orientation parameters for the photogrammetric 
processing for experiment B; we could reduce the manual effort 
during the photogrammetric processing. However, the complete 
workflow still requires manual measurements for the control 
points and statistical analysis of the results. This lack of 
automation has to be the emphasis for future work by 
development of online-triangulation methods and the automated 
measurements of control points using coded targets. 
In future work, we will also focus on the combination of LiDAR 
and image data. Since our model helicopter has a maximum 
payload of 5kg, a more powerful UAV-system (Aeroscout Scout 
B2-120; Aeroscout) will be used, which also features the flight 
control system of weControl. The combination of LiDAR and 
image data allows us to use the strength of both systems: the laser 
scanner is good in areas with less texture, while image data is 
advantageous for edge measurement and texture mapping. 
ACKNOWLEDGEMENTS 
The author thanks Kirsten Wolff and Martin Sauerbier for their 
contribution to the paper and Manuel Kaufmann for his input to 
the Randa project. 
REFERENCES 
AAAI. 2008. American Association for Artificial Intelligence. 
http://www.aaai.drg (accessed May 8 th 2008). 
Bendea, H. F., Chiabrando, F., Tonolo, F. G., Marenchino, D., 
2007. Mapping of archaeological areas using a low-cost UAV the 
Augusta Bagiennorum Test site. XXI International CIPA 
Symposium, Athens, Greece. 
Eisenbeiss, H., 2004. A mini unmanned aerial vehicle (UAV): 
system overview and image acquisition. International Archives of 
Photogrammetry. Remote Sensing and Spatial Information 
Sciences. 36(5/Wl). (on CD-ROM).