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

2.1 Manual On-Screen Digitizing
Evolving computer technology enabled digitizing interactively
which was made in the former times on digitizing tables. The
details on graphical map are traced on the screen via proper
software. The end product is a compound of many user defined
layers. The topology is created and edited by the user himself.
2.2 Object Oriented Image Analysis
In object oriented image analysis the basic processing units are
not only individual pixels but also image objects or segments.
The classifiers in object oriented image analysis are soft
classifiers that are based on fuzzy logic. Soft classifiers use
membership to express an object’s assignment to a defined
class. The membership value lies between 0.0 and 1.0, where
0.0 expresses absolute improbability and 1.0 expresses a
complete assignment to a class. The degree of membership
depends on the degree to which the objects fulfill the class-
describing conditions. One advantage of these soft classifiers
lies in their possibility to express uncertainties about the
classes’ descriptions. The basic processing units in object
oriented image analysis are objects or pixel clusters, with object
oriented approach to analyze images, the first step is always to
form the processing units by image segmentation (Yan, 2003).
After all processes mentioned above the objects on the image
can be recognized by software using pre-defined parameters.
Thus, what at manual digitizing the user carries out is handed
over to computer software. Operator intervenes in case of
making essential alterations to the parameters.
The study area, which is shown in Figure 1, is a part of
Zonguldak city, located in Western Black Sea region of Turkey.
It is famous with being one of the main hard coal mining field
in the world. Although losing economical interest, there are
several coal mines still active in Zonguldak. Area has a rolling
topography, in some parts, with steep and rugged terrain. While
partly built city area is located alongside the sea coast, there are
some agricultural lands and forest inner regions. In the study
area the elevation ranges roughly up to 400 m.

Figure 1. Study area

In this test, image part from full panoramic KVR-1000 frame
with frame number of 2252 and the viewing date of October
17”, 2000 was implemented. The first phase in the production
of KVR-1000 orthoimages in Sovinformsputnik (SIS) is the
scanning of hardcopy KVR-1000 photographs. This task was
realized by the Zeiss SCAI scanner using 7 um pixel size. For
rectification of KVR-1000 images, the PC-based digital
photogrammetric system called Ortho/Z-Space developed by
the cooperation of SIS and Russian Institute GosNIAS was
used. In this process, generally DEM from stereo TK-350
images or by the available mapping materials can be used. In
the given case, for orthoimage generation, DEM digitized from
the topographic maps of 1:100000 scale (with the height
accuracy of 20 m) was used (information from the SIS). The
used KVR-1000 orthoimage’s pixel size is 1.56 m, ellipsoid is
WGS-84, projection is UTM. It is in 8-bit grayscale. For the
purposes, large scale maps (1:1000) which photogrammetrically
produced are used. These maps date back to 1997.
Two methods have been used in this study. The first one is on-
screen digitizing which requires user intervention at the whole
digitizing process. The second method requires some settings
prior to processing. Here operator plays also a crucial role but
the intervention is slightly reduced compared to manual
methods. The method used in this study is object oriented image
analysis approach as described in the second section briefly.
The study area consists of 2 km x 1.8 km sub-image of a KVR-
1000 orthoimage covering nearly 14 km x 14 km on the ground.
Computer Aided Design (CAD) software has been used for on-
screen digitizing. While KVR-1000 image is in WGS-84
coordinate system, the 1:1000 maps are in national coordinate
system. Thus, a transformation between both systems is
necessary. The transformation has been made by polynomial
methods and yielded an accuracy of 4.5 m. Corresponding
transformation points in both systems are chosen visually.
Buildings =
Figure 2. Digitized structures and study area
On-screen digitizing result is given in Figure 2 in green color
overlapped on orthoimage. Centre lines of roads can be shown
as red color in the same figure. The digitized vector results
represent the area as seen from this overview image. For the