The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
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Parts of buildings may be occluded by other buildings, problems.
Figure 7: Shaded visualization of matched DSM with some details, upper right = DMC-image
7. Conclusion
Digital surface models in urban areas can be generated based on
large scale DMC-images, but some difficulties have to be
expected, such as caused by: radiometric problems, occlusions,
shadows and vegetation.
In the area including complex objects such as small and large
buildings and trees close to roofs, the detection and definition
of comer points in the building was poor, leading to the
possibility of errors.
But we can deduce the following results:
High accuracy of generated DSM based on DMC-images
with a standard deviation of the height between 0.8 and 1.2
GSD.
Generation of good DSM depends on the image quality
and the object visibility.
The matching parameters should be optimized according to
the characteristics of each area.
REFERENCE
Gruen, A.W. and Baltsavias, E.P., 1987: High-precision image
matching for digital terrain model generation, IAPRS, Vol 25,
No 3/1: 284-296.
Heipke, C., 1996: Overview of Image Matching Techniques -
http://phot.epfl.ch/workshop/wks96/art_3_l .html(January 2008)
Jacobsen, K., 2006: Digital surface Models of city Areas by
very High Resolution Space Imagery. EARSeL Workshop on
Urban Remote Sensing, Berlin March 2006, on CD
Jacobsen, K., 2007: Manual of program system BLUH, Institute
of Photogrammetry and Geoinformation, Leibniz University
Hannover, Germany