International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
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f; is the input state of context at time / on layer T and 7; is 
the state of context at time / on space 7. 
The evolution equation for / is as follows: 
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HR (2) 
where f is a free parameter and 0« Pi X] is chosen to 
satisfy the constraint that lr; =1, 
Spatial-temporal databases represent huge amount of data, new 
technologies of processing and a new access and management 
of large databases. 
Trutnov - u staré lanovky 
& occurence of the forest in 
1953 1970 1977 1984 1989 
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Figure 1. The result of temporal analysis is shown - the forest 
body between years1958-1989 in Trutnov neighbourhood 
(closed to power plant). Aerial data sets evaluation. 
On the other hand, specific temporal based queries should be 
used to analyse spatial-temporal oriented databases like: 
Retrieve all locations that changed between f,and f, about at 
least 1/10 of current value. 
Calculate the changes between 7; and /, on given areas. 
Compare the changes in given time strips. 
Briefly speaking, great amount of data asks for automation of 
the classification and interpretation process of spatial data 
without the necessity to collect and utilize the large training sets 
(Tyree, Long, 1997, Ocelíková, 2002). The useful approach in 
the face of such conditions is to utilize hierarchy in analytical 
process. 
2. DECISION-TREE DESIGN 
2.1 Tree Structure Design 
Decision tree is often called multi-level classifier. It reflects the 
fact that we deal with multi-class, multi-node and multi-peak 
distribution. The tree structure hierarchy simplifies the problem 
of multi-class recognition and convert it into more convenient. 
space where the step-by-step decision-making can be applied. 
The advantage of this approach is that the model construction is 
approximately low cost and it is easy understand. 
There are three main parts to solve: 
Q Tree structure design 
Q Decision rule selection 
Q Feature selection 
The relations defined by the class hierarchy cause the 
inheritance of class descriptions (attributes, relations and 
methods, it means for example the operations for the computing 
of different parameters without depending on the selected scale 
of the image, respective the size of the object) of child classes 
on one hand and semantic grouping of classes on the other. 
Inheritance: Class descriptions defined in parent classes are 
passed down to their child classes. The class can inherit 
descriptions from more than one parent class. The inheritance 
hierarchy is a hierarchy of similarities that can reduce the 
redundancy and complexity in the class descriptions. 
Groups of aggregation: It is the combination of classes to a 
class of superior semantic meaning. Beyond that, the group 
hierarchy has the direct functional implication. Each feature that 
addresses the class is automatically directed to this class and all 
its child classes in the group hierarchy. The class can be part of 
more than one group. 
Structure: Differs slightly from the other two hierarchies, 
although the structure can have parallels to the hierarchy of 
groups. Different classes can be put together in structure groups 
as a basis for the classification-based segmentation. This tool 
make possible fuse even of previously heterogeneous regions to 
single objects. 
2.1.1 Boolean Matrix 
To express any relationship of objects to each other objects we 
can use the matrix. To model the hierarchy of objects, it means 
inheritance relationship between object we can utilize Boolean 
matrices. The true will be entered in the matrix element where 
the row object (class) inherits from the column object (class) 
and false everywhere else. 
We can define the simple operations to identify the level of 
inheritance transitivity, groups of aggregation, accounting of 
restrictions and assumptions tailored exactly to the solved 
application. 
Boolean matrix expresses the inheritance of classes S1 — S9. We 
can read it as follows: S2, S5 and S8 are subclasses of S1, $3, 
S4 are subclasses of S2, S6 and S7 are subclasses of S5 and S9 
is subclass of S8. In case of multi-class decision we deal with 
sparse matrices. 
  
  
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