Pakzad, Kian 
  
6 CONCLUSIONS 
The current results of the monotemporal interpretation using CIR aerial images led to correct results for the segments of 
our test area. Applying the interpretation on grayscale images led for the most segments to the same results. This is 
important because most existing aerial images of such areas are of grayscale type. 
The extension to a multitemporal interpretation enables the distinction between more land use classes, which could not 
be interpreted without the temporal knowledge. This applies e.g. to the distinction between the land use classes area of 
regeneration and area of degeneration. The depiction of both classes in aerial images can be very similar. To 
distinguish between these classes the temporal knowledge, whether peat extraction had been carried out or not, is 
necessary. The exploitation of temporal knowledge leads to a more robust interpretation of land use classes, e.g. the 
distinction between agriculturally used area and area of regeneration (section 5.4). The use of temporal knowledge can 
therefore partly replace the need of color information. 
The presented approach was successfully used for the multitemporal interpretation of moorland. The results show that 
the exploitation of prior knowledge improves the interpretation compared to purely data driven methods. With the 
presented approach we also show a suitable way to formulate and use the prior knowledge for image interpretation. The 
explicit knowledge representation allows us to formulate prior knowledge easily in a standard language and also to 
adapt it to similar problems in a simple way. 
The research in this area will continue in different parts: The image processing operators have some parameters which 
have to be adapted manually to the used images. The aim is to do this automatically. Other parts are the resegmentation 
and the probabilities of the multitemporal interpretation. Furthermore the suitability of the used prior knowledge for 
other moor areas has to be verified. 
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