The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
The results of image segmentation are shown in the Figures 5 
and 6. Although the areas obtained are very close to the outlines 
of real objects in the source scene we sure, these results could 
be still more distinct. One of the disadvantages of watershed 
algorithm is that it finds region border biased due to 
morphological dilatation used for regions growing. 
2.4 Contours versus regions 
Next step is contours refinement with taking into consideration 
aggregated data from both areas with their borders and 
independent contours. For further images processing let us use 
three additional rules: 
delete all closed contours which are fully contained inside 
some region, 
merge all regions inside closed contours into one region, 
make more accurate borders of regions, taking the nearest 
independent contours as precise position of the border. 
Figure 7. Rural area, contours versus regions 
Figure 8. Urban area, contours versus regions 
From successive implementing of these rules the final set of 
contours showing in the Figures 7 and 8 (together with regions), 
and in the Figures 9 and 10 (together with source images) arises. 
Figure 9. Rural area, borders versus source image 
Analysis of Figures 7-10 shows that implementing of combined 
technique for every image from the presented ones gives a 
distinct set of closed contours, near to what we could expect 
from human operator. Now one should try to match the 
appropriate pairs of contours. 
Figure 10. Urban area, borders versus source image 
3. CONTOURS MATCHING 
3.1 Contour presentation 
As a result of extraction and refinement procedures in previous 
section a set of contours in the form of simple chain codes for 
every image was obtained. In further investigation, the means 
of contours presentation plays an important role for success of 
their digital comparison by any way. We have chosen for this 
purpose rational splines (Zavjalov, Y., Kvasov, B., 
Miroshnichenko, B., 1980), which shows very good behaviour 
in situations when shape of curve is not known a priori. A 
preliminary step in spline approximation is to find any valuable 
set of nodes by means of some regular procedure. A simple and 
natural way to obtain the set of nodes required is to build up a 
piecewise linear approximation for chain code. A widely known 
algorithm described in Prett, W., 1978 was used for this purpose. 
A set of polygon vertices obtained is taken as a set of spline 
nodes. 
On the basis of rational spline one then build up 1-D curve 
representation, curvature versus arc length (Davis E.R., 2005, 
Schenk, T., 1999). The form of curvature function for 2 
contours from 2 images are presented in Figures 11-14. The 
initial point of every curve is arbitrary and proper length shift is 
estimated during feature matching.