The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008 
At first step input image is smoothes with a Gaussian kernel: 
I g = G ^* 1 
Where j , I and are the smoothed image, input image and 
Gaussian kernel respectively. By introducing a point inside a 
building as sample data, the model runs and the initial curves 
are generated automatically which they are a series of regular 
circles (figure 3). 
noise. Previous models in building extraction don't have this 
accuracy in dens, irregular an attached buildings urban regions. 
A problem of our model is in detection of buildings that have 
similar spectral information with other features such as streets. 
4. ACCURACY ASSESSMENT OF THE MODEL 
In this paper, we use the McKeon's shape accuracy factor for 
evaluation of the model. In this relation the area of buildings in 
ground truth is compared with the area of buildings that is 
detected by model. 
\A-B\ 
shape accuracy = (1 - L ) * 100 
A 
Where A and B are area of a building in ground truth and area 
of its corresponding detected building respectively. Table 1 
shows the results of the model. 
shape accuracy(%) 
max 80 
min 60 
mean 75 
Table 1: the shape accuracy obtained from the model 
5. CONCLUSION 
In this paper, an optimum model of Active contour models is 
utilized for automatic building extraction from aerial images. 
This model does not need introduction of initial curves near 
edge of buildings. Results of our model applied to aerial images 
show suitable results especially in dense and irregular urban 
areas. 
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Figure 3: initial curves generation 
Step 2 
Step 3 
Figure4. Curve position in three iterations todetect buildings 
boundary 
The result of implemented model shows that the boundaries of 
buildings are detected accurately and model is not sensitive to