rlin was chosen, consisting
All images were taken at
te of that, the automatic tie
.]l. The achieved relative
in with imagery of the
r scenarios not included in
e to the generally lower
ckBird in compare to its
3), as shown in Table 2, is
pointing accuracy of the
he absolute and relative
a set of precisely measured
ints not used for bundle
ndependent reference.
ell PowerEdge T610 with
CPUs at 2.93GHz. For all
could be performed in at
> preparation time tpreperation
tep requires relatively little
| SGM processing step on
The Dunedin scenario took
arger height range, caused
"took very little time as
2 m because of the low
4 processing per square
GSDpsm, the number of
able 2 the processing times
matching and orthophoto
'nt scenarios.
tof Berlin, textured by
For aerial image processing, it has been shown (Hirschmüller
and Bucher, 2010) that the height error of SGM is around
half of the GSD, additional to the registration error. In our
experience, the same quality can be reached with satellite
images.
Figure : Reconstruction of a part of Cape Town
5. CONCLUSIONS AND OUTLOOK
It has been discussed that processing high resolution digital
surface models from satellite images on a productive level
requires a fully automatic and robust approach. This paper
presented a solution to this problem. This results in a huge
gain in productivity and cost-efficiency. From the large
number of already processed scenes, examples were given
with images from DigitalGlobe satellites. However, the
developed method has also been tested with many datasets
from an airborne line camera (Wohlfeil, 2010 and 2011).
The processing times needed on the mentioned test machine
can be reduced significantly to a very small fraction via
parallelization since most time is consumed by SGM
matching, which can run in parallel if more CPU cores or
more computers are available.
The remaining manual interaction can be reduced further.
Suitable parameters (orientation, season, etc.) from an image
database will be subject of further investigation in order to
select images automatically. We also see good chances in
refining the SRTM-based water masks by existing or new
image processing algorithms in order to get optimal results
even in regions where water covered areas change fast.
Besides water masking, it is also important to mask clouds.
As only in one of all processed scenarios clouds occurred this
issue was not treated yet. But it is regarded to be solved
easily as there are many different algorithms available that
are capable of segmenting clouds automatically due to their
high intensity values and homogenous structure.
6. ACKNOWLEDGEMENTS
We would like to thank DigitalGlobe for kindly allowing us
to use the imagery for our research.
7. REFERENCES
Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. 2008. “Surf:
Speeded up robust features,” Computer Vision and Image
Understanding (CVIU) Vol. 110, No. 3, 346-359.
Bouguet, J-Y. 2000. Pyramidal Implementation of the Lucas
Kanade Feature Tracker. Description of the algorithm. Tech.
Report, Intel Corporation Microprocessor Research Labs.
79
Bovolin, V., De Chiara, G., Migliaccio, M. and Villani, P., 2006.
Remote Sensing Technique to Estimate the Water Surface of
Artificial Reservoirs: Problems and Potential Solutions, IEEE
GOLD Remote Sensing Conference Bari
Ernst, I. and Hirschmiiller, H. (2008). Mutual Information based
Semi-Global Stereo Matching on the GPU, International
Symposium on Visual Computing, Las Vegas
Gehrig, S., Eberli, F. and Meyer, T. (2009). A Real-Time Low-
Power Stereo Vision Engine using Semi-Global Matching,
International Conference on Computer Vision Systems (ICVS),
LNCS 5815
Gehrke, S., Morin, K., Downey, M., Boehrer, N. and Fuchs, T.
2010. Semi-Global Matching: An Alternative to LIDAR for
DSM Generation?, In International Archives of the Photo-
grammetry, Remote Sensing and Spatial Information Sciences,
Vol. XXXVIII
Hirschmüller, H. 2005. Accurate and Efficient Stereo Processing
by Semi-Global Matching and Mutual Information, Proc. of
IEEE Conference on Computer Vision and Pattern Recognition,
San Diego, Vol. 2, pp. 807-814
Hirschmüller, H., Scholten, F. and Hirzinger, G. 2005. Stereo Vi-
sion Based Reconstruction of Huge Urban Areas from an
Airborne Pushbroom Camera, Lecture Notes in Computer
Science: Pattern Recognition, Proceedings of the 27" DAGM
Symposium, Vienna, Austria, Vol. 3663, pp. 58-66
Hirschmiiller, H. 2008. Stereo Processing by Semi-Global
Matching and Mutual Information, IEEE Transactions on
Pattern Analysis and Machine Intelligence, 30(2), pp. 328-341
Hirschmiiller, H. and Scharstein, D. 2009. Evaluation of Stereo
Matching Costs on Images with Radiometric Differences, IEEE
Transactions on Pattern Analysis and Machine Intelligence,
31(9), pp. 1582-1599
Hirschmiiller, H. and Bucher, T. 2010. Evaluation of Digital
Surface Models by Semi-Global Matching, DGPF Vienna
Hirschmiiller, H. 2011. Semi-Global Matching - Motivation,
Developments and Applications, Photogrammetric Week,
September 2011, pp. 173-184
Lowe, D.G. 2004. Distinctive Image Features from Scale-
Invariant Keypoints. International. Journal of Computer Vision,
60(2), pp. 91-110
Lourakis, M. and Argyros, A. 2004. The design and
implementation of a generic sparse bundle adjustment software
package based on the levenberg-marquardt algorithm. Technical
report, ICS-FORTH, Heraklion, Greece.
Shi, J and Tomasi, C. 1994. Good Features to Track. Proc. of
IEEE Conference on Computer Vision and Pattern Recognition,
pp. 593-600.
Tomasi, C. and Kanade, T. 1991. Shape and Motion from Image
Streams: a Factorization Method - Part 3: Detection and Tracking
of Point Features. Tech. Rep. CMU-CS-91-132, Computer
Science Dept., Carnegie Mellon University.
Wohlfeil, J. 2010. Completely optical orientation determination
for an unstabilized aerial three-line camera. Proceedings of SPIE
Sensors, Systems, and Next-Generation Satellites XIV, 7826.
Wohlfeil, J. 2011. Dissertation: Optical Orientation Determi-
nation for Airborne and Spaceborne Line Cameras, Humboldt
Universität zu Berlin, Naturwissenschaftliche Fakultät II
Wohlfeil, J. 2012. Determining fast orientation changes of multi-
spectral line cameras from the primary images. /SPRS Journal of
Photogrammetry and Remote Sensing, 67, pp. 45-51
Zhaohui, Z., Prinet, V. and Songde, M., 2003 *Water body
extraction from multisource satellite images”. [EEE, 0-7803-
7929-2/03