The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
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contrast closed-boundary regions of an appropriate size, ponds 
and lakes, buildings, forests, car parks or shadows. The general 
criterion of closed boundary regions is prevalent. The regions 
are often represented by their centers of gravity, which are 
invariant with respect to rotation, scaling, and skewing and 
stable under random noise and gray level variation. Region 
features are detected by means of segmentation methods (Pal, 
N., Pal, S., 1993). The accuracy of the segmentation can 
significantly influence the resulting registration. The 
researchers took much efforts to develop segmentation 
technique robust to various changes in shooting conditions 
(Zitova, B., Flusser, J., 2003). 
Figure 1. Rural area 
Figure 2. Urban area 
Combining merits from two groups of algorithms, developed 
for lines and regions separately one can try to construct the 
improved technique for extracting contours of desired quality. 
In this paper two control pairs of images are used (see Figure 1, 
Figure 2) to demonstrate the main steps and the results of the 
samples recognition technique. 
2.2 Extraction of contours 
For edges extraction Sobel filter and Canny detector was used 
as basic technique. Then morphological algorithms was 
followed for pruning the tales from contour lines. The results of 
independent contour extraction are shown in the Figures 3 and 4. 
This technique produced a large number of edges, but the result 
is still too noisy and spotty. 
Figure 3. Rural area, edges 
Figure 4. Urban area, edges 
2.3 Image segmentation 
Image segmentation was performed by watershed algorithm, 
which is known to produce many small areas at the preliminary 
stage (Shafarenko, L., Petrov, M., Kittler, J., 1997). Then 
closest areas joined according to some criteria. For images 
under consideration, fusion was performed according to mean 
gradient difference along the borders. Second rule for fusion 
was characteristic object size, which amounts 200 pixels for 
images under consideration. Pre-processing of images with 
truncated median filter produces the resulted segmentation of 
better quality. 
Figure 5. Rural area, regions 
Figure 6. Urban area, regions