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(a) urban area in data set 
  
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(b) urban area in data set 2 
Fig.4 Urban extraction maps of the study areas using the K-means method. 
Comparing fig.3 and fig.4, it is obvious that the proposed 
method has much better performance. In the results of K-means 
algorithms the streets at the urban area are mostly classified as 
urban area. More over it can be seen that the proposed method 
have classified both the urban and non-urban area more 
accurate. 
It should be noted that a Lee filter is chosen to reduce speckle 
effects with a window size of 5x5. According to Fig. 3, 
qualitative analysis shows the efficiency of the implemented 
method in different test areas. Man-made and urban areas are 
properly distinct from other types of land covers in the images. 
The efficiency of the methods is related to the textures which 
are extracted from the original SAR image as well as the used 
inhomogeneity parameter. By analysis of the inhomogeneity 
parameters in Fig. 2, one can conclude that, (in the areas 
without buildings, is very close to zero and both its mean and 
variance are small. One important point in the implemented 
framework is the widow size which is used in texture and 
inhomogeneity parameter computations. In other word, there is 
a trade-off in choosing the window size for the texture analysis. 
Indeed, as the window size increases, the texture feature is 
better estimated in terms of statistics robustness, but the 
uncertainty area between two different textures also gets larger, 
and edges are not accurately localized. A window size of 15 x 
15 pixels has proved to be a good compromise for images with 
spatial resolutions ranging between 5 and 20 m [17]. However 
using of the fuzzy logic seems to be interesting algorithm in 
computing the window size. 
4. CONCLUSION 
In this paper, a technique for the detection of the man-made 
structures using very high resolution SAR images is proposed. 
Basically, the procedure starts with the calculation of different 
textures from SAR data. The textures are extracted from the 
amplitude images. Since not all these textures are informative, a 
feature reduction step is used. Among different feature 
extraction methods, in this study the well-known principal 
component analysis (PCA) has been adapted. Then a simple and 
powerful estimator called inhomogeneity parameter for the 
urban structures highlighting is computed and multiplied by the 
first component of PCA. Finally the urban areas are extracted 
from the multiplied image using a binary decision method. 
Results on the very high resolution (VHR) TerraSAR-X images 
show that the method has high efficiency for unsupervised man- 
made structure extraction. However, the framework needs to be 
applied to an enormous SAR image database and also to the 
different SAR sensors image. 
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