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Title
Proceedings, XXth congress


International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004
Following the segmentation with small scale values as applied
in Figure 6 the classification is resulted in for a series of
buildings as shown in Figure 8. Although the building blocks
should have been separated from each other, the buildings are
classified altogether apparently. The overlapping manually and
automatic digitized objects are shown in Figure 9.
It is too normal that, there can be some inconsistency between
classifiers fail at separating two different buildings as one
building as seen in Figure 9 marked with 1. Besides, some
buildings can not be extracted from the image, although these
constructions can be digitized manually. This situation can be
seen in Figure 9 marked by 2. Originally these three buildings
exist in the 1:1000 line maps. However, some buildings like in
Figure 9 marked by 3 have the same size obtained by manual
and automatic digitizing.
5. CONCLUSION
In this study, manual on-screen digitizing and the automatic
object oriented image analysis methods have been compared
using KVR-1000 orthoimage. By manual method, almost all
building and road details that are available or not available
could be derived. Although the effective pixel size of KVR-
1000 orthoimage is about 2 pixel, experience and function of
operator are the main factors on the success rate. However,
accuracy of the coordinate transformation of about £12 m does
not provide the required position accuracy. The reason for this
is that the KVR-1000 orthoimage was generated by the DEM
with 20 m height accuracy. As a rule of thumb 10 times of the
pixel size gives the scale factor (Jacobsen, 2002). For KVR-
1000 case 10 times of the pixel size is 15.6 m and this
corresponds to 1:16000 map scale. Individual structures in a
forest can be located significantly due to their distinct grey
values using KVR-1000 images. Such a study was made by
Karathanassi et al. (2003). Their concern was not the geometric
accuracy of the classification. But our study has attempted the
accuracy potential of from KVR-1000 image digitized vector
maps. The study comes to the conclusion that pixel size does
not dictate the map scale of end product to be extracted from
the satellite images such as KVR-1000.
Expected success rate could not be reached on the KVR-1000
ortho-image using eCognition 3.0 object-oriented image
analysis software not enough contrast, monochromatic image,
and negative influence of DEM on orthoimage generation fails
the segmentation phase, then the following classification
produce did not work as efficient as possible. In contrast to
automatic method, manual, method produced expected success
for the object extraction purpose.
6. REFERENCES
Bjorgo, E. 2000. Using very high spatial resolution
multispectral satellite sensor imagery to monitor refugee camps.
International Journal of Remote Sensing, 21(2), pp. 611-616.
Jacobsen, K., 2002. Mapping with IKONOS images. In:
EARSeL Symposium “Geoinformation for European-wide
Integration", Prague, Czech Republic, pp. 149-156.
Karathanassi, V., lossifidis, Ch. and Rokos, D., 2003. Remote
sensing methods and techniques as a tool for the
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Kostka, R., 2002. The world mountain Damavand:
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Parker, J.R., 1997. Algorithms for Image Processing and
Computer Vision. Wiley Computer Publishing, pp. 3-5.
Robinson, A.H., Sale, R.D., Morrison, J.L. and Muehrcke, P.C.,
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SOVINFORMSPUTNIK, 2004. Information from official web-
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Topan, H., Buyuksalih, G. and Jacobsen, K., 2004. Comparison
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Yan, G., 2003. Pixel based and object oriented image analysis
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7. ACKNOWLEDGEMENTS
Parts of the presented results have been supported by
TUBITAK, Turkey and the Jülich Research Centre, Germany.
The authors wish to thank Dr. Gurcan Buyuksalih, Dr. Karsten
Jacobsen and M. Guven Kocak for their supervisions during this
study.
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