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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004

space (Parker, 1997). But in general, the grey value profile will
not be sharp due to the imaging sensors. In Figure 5a, the edge
lies in left-right direction, the edge in 5b lies in diagonal and the
edge in 5c lies in upper-down direction in the image. All of
them are the edges between roof (white) and grass (dark) and
the grey value profiles are not sharp like in the object space. For
analysis, the EDGE module of BLUH program system of
Hannover University has been used. It is realized that the
profiles are not so smooth. However, the profile of diagonal
edge is more smooth than a and c. Figure 5d is an edge of
swimming pool and the dark side is the water and the profile of
diagonal edge.is the sharpest among the samples. The grey
value profile gives the effective pixel size from the differences.
The width of point spread function at 50% height can be used as
effective pixel size (Topan et al., 2004). For KVR-1000 image
used in this paper, the effective pixel size is nearly 2.7 m. This
means that, during the digitizing process, the operator senses
approximately 2 pixels instead of I pixel. This situation will
effect the digitization negatively.





Figure 6. Created segments
Starting point of object oriented approach using the commercial
software eCognition v3.0 is to create segments which are basis
for building objects. The output of the segmentation step is
shown in Figure 6. The boundaries show equi-characteristic
cluster of pixels. The characteristics are defined by parameters
before processing.


147
Figure 7. Mixed segments
A lot of problems occurred in the course of processing. One
problem experienced is spreading of grey values over
neighboring pixels due to buildings having the same
characteristics would have been classified into the same class.
But this is not the case because some buildings are shadowed by
the adjacent buildings. The similar reflectance properties of
different neighboring objects give rise to missegmentation of
these different classes. This situation is shown in Figure 7.

Figure 8. Objects in buildings class
Due to above stated problems and selecting small scale
parameter the real world cannot be extracted exactly. On the
contrary setting large scale parameter values leads to clutter of
buildings. Several experiments are carried out with different
parameters settings but the expected results are not satisfactory.
The most acceptable output is obtained using mean grey value
criterion. Grey values falling below 185 are disregarded and
some extra.setting values are used in the framework of this
study. The classification results obtained hereafter are shown in
Figure 9..

Figure 9. Overlap of manual and automatic digitized objects