administrative units where the evaluation is provided and 
considered the relations and contextual and temporal 
background for the data processing. 
Object-oriented image analysis is based on an object-oriented 
approach to image analysis. In contrast to the classical image 
processing method, the basic processing units are image 
objects or segments. Further motivation for the object- 
oriented approach is the fact that the expected result of most 
image analysis tasks is a set of real world objects. The 
topological relations of single or adjacent pixels are given by 
the raster implicitly. The association of adjacent image 
objects must be explicitly worked out, in order to address 
neighboured objects. 
The increasing resolution of the sources results in the 
increasing number of objects of course and moreover the 
complexity of object structuring hierarchy is rapidly growing 
too [9]. We consider: the house as well as the block of 
houses, or the type of the urban site. It is very similar in case 
of trees: scattered trees can create one object and dense 
standing trees another one and what about the forest? 'The 
huge amount of data asks for automation of the classification 
and interpretation processes of the spatial data. 
There are many reasons for the knowledge-based spatial data 
network building and sharing. The  pixel-oriented 
classification can be accepted as the pre-processing phase 
that is followed by object-oriented contextual classification. 
This opinion is supported by the long experience with texture 
analysis and it represents only the local type of context 
incorporation into the processing and it represents the lowest 
level of contextual modelling. 
2.2 Object hierarchy 
The average resolution of image objects can be adapted to the 
scale of interest and resulting information can be represented 
in the scale based on the average size of image objects. This 
fact is coherent with the hierarchical networking and 
representation of image objects. In the hierarchical structure 
each object knows its neighbours, sub objects and super 
objects. 
We distinguish three types of elemental object features: 
attribute (physical properties of objects that follow from real 
world or image or different information layers related to the 
object), fopological features (describe the geometric 
relationships between the objects or the whole scene) and 
context features (represent the objects’ semantic 
relationships. The gardens are inside the urban area; the 
island is surrounded by the water, and so on. It means 
between class (object) relationships. 
The features are the means to assign an object to the certain 
class. They can be used simultaneously or separately as well 
as in exactly defined hierarchy and their essence comes from 
spectral, textural, local, global, temporal, contextual or any 
other of the available property of image entity. 
They help, together with the class hierarchy that makes 
possible to use the inheritance, to create groups of the object 
and set up the structure, to give additional meaning to the 
classes of objects. The class hierarchy defines the 
requirements that an object must meet to be assigned to the 
certain class — the first step of segmentation. 
124 
The class hierarchy, which can be continually changed during 
the processing, it is the base of knowledge for the image 
object classification. In many applications the desired geo- 
information and the objects of interest are than extracted step 
by step, by iterative process of classifying and processing. It 
is very similar to human image understanding processes. This 
kind of circular processing results in a sequence of partial 
states, with an increasing differentiation of the classification 
result and the increasing abstraction of the original image 
information. 
a 
  
  
  
  
Figure 1. Aerial data - identification of swimming pools. 
On the each step of the abstraction new information and new 
knowledge is generated and can be used beneficial for the 
next analysis step. High beneficial is the fact, that after 
successful analysis, a lot of interesting, additional 
information can be derived. 
The class hierarchy approach is also very good tool to test the 
ability of students to understand the task and process the final 
study for the subject GIS. 
  
  
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Figure 2. Swimming pools compared with parcels — 
identification of owners. 
The different segmentation techniques can be used to 
construct a hierarchical network of image objects and each 
level in this hierarchical network is produced by a single 
segmentation run. 
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