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2. BUILDING EXTRACTION APPROACH 
2.1 Principle 
The presented paper tends to extract building using object based 
image analysis considering human visual system workmanship. 
The proposed algorithm considers a phenomena as a building 
while observing a satellite image in an object-based approach 
(not a pixel-based approach) that: 1) has a regular geometric 
shape, 2) has homogenous building body and a significant 
variation in transition to close neighbourhood, and 3) is high 
and consequently has a direct neighbourhood with shadow. 
In this paper, those features that have derived from this 
definition are so called stable features. Stable features in a 
general algorithm may be implemented on images from different 
arcas and with different sensors as a stable part. In this general 
algorithm, since buildings shape and construction materials vary 
even in the same geographical regions, a flexible core is 
proposed to cover the image under study. Features leading to 
optimal separation of building class and other classes (two by 
two) in the image are extracted in this section. This paper 
classifies these features as variable features. To extract these 
features, it is essential to use an analysis tool. SEaTH analysis 
tool presented by Nussbaum has been used in this paper. In this 
method by using Jeffries-Matusita measure, features are 
extracted as optimal features in appropriate separation of 
probability distribution function for the training samples 
belonging to different classes. 
Thercafter, using aforementioned method and choosing image 
objects belonging to building class and other classes detected in 
the image, features leading to optimal separation between 
building class and other classes and the required threshold are 
determined for reaching such separation. 
Algorithm mentioned above is shown in the following figure: 
Segmentation 
  
  
Faaturs Analysis 
Stabile Features & Variable Features 
  
  
  
  
  
Building Extraction 
   
   
  
Past-processing 
   
  
Output 
image 
Figure l. Workflow of the proposed method 
55 
2.2 Data 
The described methodology has been applied on QuickBird 
multi spectral images of an urban area in Isfahan city (figure 2). 
As observed, this image includes buildings with different size, 
roof, shape and arrangement. 
    
Figure 2. The original image 
2.3 Segmentation 
At the first stage an image to be analysed is segmented into 
individual image objects in an object based approach 
(Nussbaum et al, 2008). The image pixels from the image are 
grouped to form objects in a segmentation process. The created 
image objects should present the objects in reality. In this 
research multiresolution segmentation algorithm has been used 
to create image objects. multi-resolution segmentation is a 
bottom up region-merging technique starting with one-pixel 
objects. In numerous subsequent steps, objects are grouped into 
a larger object based on spectral similarity, contrast with 
neighbouring objects, and shape characteristics of the resulting 
object. In each step, that pair of adjacent image objects is 
merged which results in the smallest growth of the defined 
heterogeneity. If the smallest growth exceeds the threshold 
defined by the scale parameter, the process stops (Benz et al., 
2004). In this algorithm, the proximity degree of gray value to 
each other in an image object is determined by Scale Parameter. 
The bigger this scale parameter value, the smaller this proximity 
becomes and so the size of the objects will be bigger. In this 
study, in a trial and error process, in order to obtain optimal 
results, the value of this parameter has been considered 40. It is 
to be noted that choosing appropriate scale parameter prevents 
over and under segmentation, though accessing ideal 
segmentation considering this fact that there is numerous image 
objects with different heterogeneity in a satellite image is not 
possible. Selected scale parameter considers colour and shape 
factor simultaneously. Though, in many cases the colour factor 
is the most effective parameter in the creation process of image 
objects, considering shape factor leads to quality improvement 
of the produced objects. In this research, colour factor is 
weighted with 0.7 and shape factor is weighted with 0.3. It is 
mentioned that shape factor is divided into two parameters of 
smoothness and compactness and both weights are considered 
0.5 in this case study (figure 3 and figure 4).