The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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also shadows of buildings around these roads lie on them. In a 
low resolution image the size of pixel could be bigger than the 
width of road so the lower the image resolution the harder to 
detect the roads (Figure 2). 
Figure 2: Roads that extracted by using automated methods. 
2.2. Semi- Automated Methods 
Filtering 
Spatial frequency is described as the number of variations 
among pixels’ values in a specific region over the raster dataset. 
If this variation is low that image can be described as low 
frequency image, otherwise if variation is high the image 
described as high frequency image. 
High pass filters are used for increasing the spatial frequency of 
images while Low pass filters are used for reducing or 
suppressing the spatial frequency of images. In filtering 
processes each pixel evaluated with particular number of its 
neighbors pixels and depends on weight the new value of each 
pixel is computed and then assigned to each one. 
PREWITT FILTER 
SOBEL FILTER 
Quickbird 
Quickbird 
Ikonos 
Ikonos 
Aster 
Figure 3: High pass filtered (Prewitt and Sobel) images. 
Classification 
In this study in order to detect the roads edge detection filters 
which are the types of high pass filters (Prewitt and Sobel) are 
conducted to each satellite images having different spatial 
resolution. In this way spatial frequency of each image was 
increased so the roads were highlighted and then extracted from 
each image (Figure 3). According to this processes’ results, in 
high resolution images such as Quickbird and Ikonos the main 
and secondary roads are detected more accurately, however in 
low resolution images (Spot, Aster and Landsat-ETM) the 
details are reduced and detection of roads become more difficult 
therefore at some region only the main roads could be detected. 
Classification can be described as, grouping image pixels into 
categories or classes to produce a thematic representation. 
Classification can be used in thematic maps or can be further 
incorporated into digital analysis. It can be performed on single 
or multiple image channels to separate areas according to their 
different scattering or spectral characteristics. Digital image 
classification procedures are differentiated as being either 
supervised or unsupervised. 
In this study Maximum Likelihood classification technique was 
applied four images (Quickbird, Ikonos, Aster, and Landsat)