Full text: Technical Commission IV (B4)

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Figure 6. Automatic cut lines creation 
If the images contain clouds a special module is used for their 
automatic detection. This algorithm includes a successive 
filtering of pixels — by intensity, deviation from the grey color, 
dispersion and heterogeneity. The last function measures the 
difference between changing of intensity along each axis and its 
variation. At the last step the remaining pixels are composed in 
components and their boundaries are calculated as polygons. 
  
Figure 8. Cut lines around clouds and ortho mosaic without 
clouds (no colour balancing for better visualization) 
One of the unique algorithms we use to make ortho mosaics 
look better is the usage of tie points that are calculated along cut 
lines using standard cross-correlation algorithm. The images can 
be stitched by a limited predefined value using these tie points 
to eliminate discrepancies along seams (cut lines) as illustrated 
on the following picture. 
Figure 9. Tie points to eliminate residual discrepancies along 
cut lines 
5. CONCLUSION 
Orthophoto production from VHR and HR sensors using RPC 
and precomputed DEM can be effectively run on specialized 
computer clusters with good scalability in fully automatic mode. 
A special adaptation of algorithms and software is needed for 
cluster environment. These algorithms are implemented in 
PHOTOMOD HPC Edition DPW. The developed algorithms 
are now extended for distributed GPU processing in cluster 
environments, for DTM computing and filtering, for special 
control function allowing to share the cluster between several 
workgroups. The software module for integration with GIS 
systems, databases and geoportals is also developed. 
REFERENCES 
Cheng, P., Toutin, Th., Zhang, Y., Wood, M., 2003. QuickBird- 
geometric correction path and block processing and datafusion. 
Earth Observation Magazine, 12(3), pp. 24-30. Grodecki, J., 
Dial, G., 2001. IKONOS Geometric Accuracy. Proceedings of 
Joint Workshop of ISPRS Working Groups I/2, I/5 and IV/7 on 
High Resolution Mapping from Space 2001, University of 
Hanover, Hanover, Germany. 
Fraser, C.S., Ravanbakhsh, M., 2009. Georeferencing Accuracy 
of Geoeye-1 Imagery. Photogrammetric Engineering & Remote 
Sensing, 75(6): 634-638. 
Grodecki, J., Dial G., 2003. Block adjustment of high resolution 
satellite images described by rational polynomials. 
Photogrammetric Engineering & Remote Sensing, .69(1), pp. 
59-68. 
Kay, S., Spruyt, P. and Alexandrou, K., 2003. Geometric 
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Photogrammetric Engineering and Remote Sensing, 69, pp. 
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Titarov P.S. 2007. Cartosat-1 stereo orthokit data evaluation. 
Proceedings of ISPRS International Symposium “High 
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01.06 2007 Hannover, Germany 
Toutin, T., Cheng, P., 2002. Quickbird — a milestone for high 
resolution Mapping. Earth Observation Magazine, 11(4), pp.14- 
18. 
Toutin, Th., R. Chénier, Y. Carbonneau, 2002. 3D models for 
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