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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.
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