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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
subclass (es). With, respect to the multi-scale behaviour of the 
objects to detect, a number of small objects can be aggregated 
to form larger objects constructing a semantic hierarchy. 
Likewise, a large object can be split into a number of smaller 
objects which basically leads to two main approaches of image 
analysis: A top-down and a bottom-up approach (see 
eCognition User Guide, 2003 and Benz, U., et al., 2003). In 
eCognition both approaches can be realised performing the 
following steps: 
o Creating a hierarchical network of image objects 
using the multi-resolution segmentation. The upper- 
level image segments represent small-scale objects 
while the lower-level segments represent large-scale 
objects. 
o Classifying the derived objects by their physical 
properties. This also means that the class names and 
the class hierarchy are representative with respect to 
two aspects: the mapped real-world and the image 
objects’ physically measurable attributes. Using 
inheritance mechanisms accelerates the classification 
task while making it more transparent at the same 
time. 
*  Describing the (semantic) relationships of the 
network's objects in terms of neighbourhood 
relationships or being a sub- or super-object. This 
usually leads to an improvement of the physical 
classification res. the class hierarchy. 
* Aggregating the classified objects to semantic groups 
which can be used further for a so called 
‘classification-based’ segmentation. The derived 
contiguous segments then can be exported and used in 
GIS. The semantic groups can also be used for further 
neighbourhood analyses. 
These steps describe the usual proceeding when working with 
eCognition. While the first two steps are a mandatory, the latter 
two steps may be advisable according to the user's objectives 
and content of the image. : 
In the segmentation phase, following parameters should be 
assigned as accurate as possible, of course, suiting with the 
reality. 
Scale parameter: this parameter indirectly influences the 
average object size. In fact this parameter determines the 
maximal allowed heterogeneity of the objects. The larger the 
scale parameter the larger the objects become. 
Color/Shape: with these parameters the influence of color vs. 
shape homogeneity on the object generation can be adjusted. 
The higher the shape criterion the less spectral homogeneity 
influences the object generation. 
Smoothness/Compactness: when the shape criterion is larger 
than 0 the user can determine whether the objects shall become 
more compact (fringed) or more smooth. 
Segmentation phase is folloed by the classification of images. 
¢Coginition software offers two basic classifiers: a nearest 
neighbour classifier and fuzzy membership functions. Both act 
as class descriptors. While the nearest neighbour classifier 
describes the classes to detect by sample objects for each class 
Which the user has to determine, fuzzy membership functions 
describe intervals of feature characteristics wherein the objects 
do belong to a certain class or not by a certain degree. Thereby 
Sach feature offered by eCognition can be used either to 
describe fuzzy membership functions or to determine the 
feature space for the nearest neighbour classifier. A class then is 
described by combining one or more class descriptors by means 
of fuzzy-logic operators or by means of inheritance or a 
combination of both (see Figure 1). As the class hierarchy 
should reflect the image content with respect to scale the 
creation of level classes is very useful. These classes represent 
the generated levels derived from the image segmentation and 
are simply described by formulating their belonging to a certain 
level. Classes which only occur within these levels inherit this 
property from the level classes. This technique usually helps to 
clearly structure the class hierarchy. 
  
  
     
    
   
    
   
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Figure 1. Hierarchical network of image 
   
6 
3. STUDY AREA AND IMAGE DATA 
The test site is Zonguldak and its close vicinity, located in 
Western Black Sea region of Turkey. It is famous with being 
one of the main coal mining area in the world. Although losing 
economical interest, there are several coal mines still active in 
the region. Testfield has a rolling topography, in some parts, 
with steep and rugged terrain. While urbanized part is located 
alongside the sea coast, there are agricultural and forested areas 
inner regions. The elevation ranges roughly up to 1800m. 
  
  
  
  
  
  
  
  
  
Figure 2. Landsat ETM+ image (band 3,2,1) of the study area 
For the analysis, Landsat-7 ETM image (see Figure 2) covering 
the test site and taken on 04.07.2000 has been utilised. At the 
processing phase, all spectral channels expect thermal one were 
used and their properties are given in Table 1. Reference 
datasets employed during the classification procedures of 
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