2.4 Feature Extraction 
As mentioned earlier, stable features are defined and variable 
features are extracted in this stage. In this paper, considering 
building definition, characteristics such as regular geometric 
shape, strong edge, and neighborhood with shadow are 
considered as stable features that are almost implemented in all 
cases by some little changes. 
In this image considering this fact that all buildings in the image 
are rectangular, the similarity of objects to rectangle is 
considered as one of the stable features. In order to extract 
strong edges, canny edge detection algorithm with appropriate 
threshold has been utilized that results in keeping the objects 
that belong to building class. In the next stage, shadow is 
extracted by the rules that presented by Nussbaum (figure 5). 
Direct neighborhood with shadow is considered as one of the 
stable features in this algorithm. This feature derives from 
inherent characteristic of building as a high phenomenon. 
Features presented by Nussbaum to detect shadow are as 
follows: 
Features Threshold 
Ratio B4 20.199 
Relative Border to brighter neighbors =1 
Table 1. Classification rules of shadow class presented by 
Nussbaum 
    
F igure . Detected shadows 
Further, variable features that lead to optimal building detection 
are extracted by SEparability and THresholds tool. The feature 
analyzing tool SEaTH identifies characteristic features with a 
statistical approach based on training objects. These training 
objects represent a small subset out of the total amount of image 
objects and should be representative objects for each object 
class. The statistical measure for determining the representative 
features for each object class is the pairwise separability of the 
object classes among each other. Subsequently, SEaTH 
calculates the thresholds which allow the maximum separability 
in the chosen features (Nussbaum et al., 2008). Here, choosing 
training samples of building class and other classes in the image 
such as vegetation, road, wasteland, etc, features that result in 
optimal separation of probability distribution function between 
building class and other classes (two by two) are determined as 
optimal features with appropriate threshold. 
Extracted variable features from this image have been presented 
in the following table: 
Features Threshold 
Ratio B2 »0.338 
Ratio B4 «9.229 
Mean NDVI «0.019 
Length «80 
Rectangular Fit »0.8 
GLCM Homogeneity »0.07 
Area «1200 
Table 2. Extracted variable features in this case study 
Objects qualifying all the above conditions are identified as 
building based on extracted features (stable and variable). 
3. EVALUATION AND DISCUSSION 
As mentioned before, algorithm presented in this paper as a 
general algorithm can be implemented on high resolution 
satellite imagery in different areas. Central core of this 
algorithm includes stable and variable features. In this section, 
stable features have been derived from inherent characteristic of 
building. Variable features are also extracted by an analysis tool 
for optimum separation of building class from other classes in 
the studied image. Using stable and variable features together in 
a central core provide the possibility to generalize this 
algorithm. It is worth mentioned that changing the threshold 
value of stable features for extraction of strong edges and/or 
proximity degree of pre-defined geometric shape and even any 
shape other than rectangle prevents the user from making no 
changes in these features. Of course, this problem seems to will 
limit complete transferability of classification model to other 
data sets. 
It is to be noted that determining Scale Parameter for 
segmentation in a trial and error process according to 
importance of the produced objects in all the next stages, giving 
a solution to obtain the scale parameter in an automatic process 
has a significant effect on algorithm automation process. 
Though, in this algorithm, it has been tried to extract all 
buildings in the image by considering stable and variable 
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