The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008 
Fig. 5 is another example of multi-spectral IKONOS image of 
Lavasan (4m resolution and 169x162 pixels) in semi-urban 
region. In this figure square object that is the place of 
intersection of three roads, is shown. Again the described 
procedure of road extraction is implemented on the image. As 
you see in these figures, the cause of producing gaps on the 
roads is existence of obstacles such as trees, curbs and cars . 
these gaps are filled using morphological operators. 
The most important problem in fig. 5 is limitation of MST 
algorithm in closing loops. This restriction causes missed links 
in extraction of cyclic objects like squares which are shown in 
fig. 5g by yellow lines. This restriction leads the extraction 
process to become semi-automatic and supervision of human 
operator for complement of missed links is compulsory. 
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CONCLUSIONS 
In this paper a semi-automatic road extraction system based on 
combination of different feature extraction techniques 
containing FCM and C-Means clustering technique, 
morphological functions and graph theory is proposed. 
Input images of this system consist of multi-spectral and pan- 
sharpened IKONOS images of Lavasan city in Iran (with 
respectively 4 and 1 meters spatial resolution). 
The main advantage of this proposed system is achievement of 
it in extracting different shaped roads such as straight, spiral, 
junction and square and attaining acceptable precisions in order 
to updating road maps. The only drawback of this system is 
limitation in completely extraction of road center line in place 
of squares and closed loops. So supervision of human operator 
for completing missed links and closing the loops is inevitable. 
Attaining mean overall accuracy (OA) of 98.2% and Kappa 
coefficient of 86.26% in classification of image to road and 
non-road classes, and also mean RMS error of 0.64 pixel in 
comparing automatic extracted road centerline with manual 
extracted one, are a good criterion of proposed system success 
in semi-automatic extraction of road. 
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