The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
488
cell which has the minimum distance and updating the heap for
every image cell addition or extraction.
It is purposed by this storage structure to do the following big
volume processes; reaching the image cells, testing and
calculating the spread, marking image cells and storing the
marked image cells.
3.4 Conversion from raster to vector
The algorithms of conversion from raster to vector are valid for
the 1 bit image files which contain two types of data (0 or 1)
(ESRI 1997). So that, in developed software, an image string is
made that the marked pixels are full, the others are empty after
completing the processes; selecting and marking the image cells.
Making this image string is a masking process. This mask raster
image is afterwards recorded as 1-bit (1 color) raster in BMP
format. Visual C++ code (URL 1) which is available on internet
for conversion from mask raster image to vector data is restored
and new opportunities are added. Centers lines and border lines
of the details converted to vectors one by one from raster data
with a functional interface by establishing a connection with
main program and a coordinated vector data is made by entering
the left-bottom comer coordinates and both dimension (x,y)
image resolutions in main program. Additionally the
opportunity of making the vector data to the required
smoothness by entering the tolerance is supplied in the
functional interface. Density of the vertex points, of the vector
data which are made by conversion from raster to vector by
entering the smoothness tolerance, is adjustable (Eker, 2006).
If the break point tolerance is chosen to be zero, all pixels are
included to calculate without any smoothing. In case of the
incidence of an increasing breakpoint tolerance, pixels with
increasing intervals are taken into account instead of all pixels
and the final vector is smoother. But over-increasing the
tolerance level may cause failure on the accuracy (Eker, 2006)
4. CONCLUSIONS
In comparing both production systems, the recommended one
provides approximately 5 working days in saved time. From the
production duration point of view, this gain can not be
neglected.
While making feature extraction in orthophoto images and using
the recommended production system, extraction of some
features can be difficult, especially for some line features and
those defined by their height values (electrical lines, towers,
minarets etc.) which cannot be evaluated at first glance. Also,
some features like dry streams become hard to be observed.
Therefore, those features that are extracted on orthophoto
images and have their height values from DEM, should
absolutely be checked and completed by overlapping on stereo
models. In addition to this, height errors must be corrected. As
an example, information about the numbers of the point features
which are evaluated just only from orthophoto image is given in
Table 3.
Feature
Stereo
Mono
Ratio
Building
1142
986
%86
Tree
4796
2571
%53
Bush
336
32
%10
Sheep-fold
23
18
%78
Stone
342
222
%64
Spring
5
1
%20
Culvert
100
60
%60
Fountain
5
2
%40
Water Reservoir
6
3
%50
Industrial Building
10
10
%100
Mosque
6
4
%66
Governmental
Building
8
3
%38
School
9
1
%11
Lean-to roof
3
3
%100
Antenna
3
2
%66
Table 3: Differences which resulted from the extraction of point
features using both stereo and mono images.
In this study; the accuracy of the features which were digitized
by using the suggested map production system, was investigated.
For this purpose, 35 common Ground Control Points (GCPs)
were selected in both maps, which were produced by using the
existing and the suggested systems. The coordinates of the
GCPs were measured in both maps and the RMS errors were
calculated. At the calculation, the coordinates derived from the
map of the existing production were accepted as reference.
In planimetric coordinates, ± 1 meter accuracy was achieved
while the vertical accuracy was determined as ±3 meters. The
results of the accuracy assessment are good enough for
1Y25.000 scaled topographic maps but it should be taken into
consideration that these results are reliable only for this test map
area or for similar topographies. So as to have more conclusive
results, it would better to apply similar studies for the areas that
have different topographic characteristics.
Finally, it is believed that the software can be more useful for
semi-automatic feature extraction if the deficiencies listed
below are eliminated
Incorrect feature extraction might occur if the appropriate
tolerance limits are not introduced to the software.
When the images with the very big sizes are used, software
errors may happen because of the requirement of much memory.
The quality of images significantly affects the performance of
contrast and noise algorithms.
Surface and pattern characteristics of the line features also
effect the algorithm conclusion.
If this software is used in the production flow then advanced
filters like anisotropic diffusion and edge enhancement
algorithms should be integrated. Also integrated should be
different interpolation methods to fill the gaps that occur
because of the obstacles. Availability of image pyramid
methods should also be investigated to have better results.
REFERENCES
Eker, O., 2006. Semi-automatic extraction of line features from
aerial photographs, Ph.D. Thesis, ITU Institute of Science,
Istanbul.
ESRI, 1997. ARC/INFO User’s Guide Cell-Based Modelling
With GRID, Redlands, USA.
Sethian, J.A., 1998. Fast Marching Methods and Level Set
Methods for Propogating Interfaces, von Karman Institute
Lecture Series, Computational Fluid Mechanics, Belgium.
URL 1, 2006, Raster to Vector Transformation Program,
http://www.xmailserver.org/davide.html