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ISPRS Commission III, Vol.34, Part 3A »Photogrammetric Computer Vision“, Graz, 2002 
  
6. INTERPRETATION OF MOORLAND 
6.1 Prior knowledge about moorland 
In this section some background knowledge about industrially 
used moorland is given (see also Figner & Schmatzler, 1991). 
Following that the implementation of that knowledge into 
semantic nets, the knowledge representation language of our 
system, is shown. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
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Figure 2. Semantic net for greyscale images 
Originally, moors were upland moors. In Germany these have 
practically vanished. Today, in the former upland moors 
agricultural areas, forests and areas of regeneration or 
degeneration are found. The most important industrial use of 
moorland is peat extraction. To enable peat extraction in a moor 
the ground must be drained first. For this purpose ditches must 
be dug. Thus, the water level decreases, and the area begins to 
degenerate. The vegetation changes. During degeneration the 
vegetation is inhomogeneous and irregular. Then, peat 
extraction is possible. Usually harvester machines are used for 
this task. These machines leave two straight tracks on the 
ground, which in aerial images can be recognised as parallel 
lines. It is possible that peat works stop for a short time, and 
then continue again. After peat works have finished, 
regeneration of the moorland can begin. In most cases people 
simply stop working the land and leave it to regenerate, which 
eventually results in increased vegetation. Hence, vegetation 
can be found again on these areas, especially birches, because 
of the dry ground. Remains of tracks from the harvester 
machines may still be found. To start up regeneration in the 
direction of the original moorland, sometimes supporting steps 
are carried out, as for example filling up of ditches, and trees 
are removed to raise the water level. If the water level further 
rises trees die, and a homogeneous vegetation without trees 
appears. 
A representation of the temporal part of this knowledge can be 
seen in the state transition diagram in figure 1. The 
monotemporal part is represented in semantic nets. Figure 2 
shows one of them, designed for the interpretation of greyscale 
images. At the top the moor classes are shown. Below, their 
obligatory parts contain the features and structures, which have 
to be found. The nodes in the greyscale aerial image layer 
describe the appearance of features and structures in the aerial 
images. These nodes are connected to the feature analysis 
operators (described in section 4). 
As described below the interpretation of greyscale and colour- 
images needs different semantic nets. The semantic net for 
greyscale images is able to distinguish eight different states, and 
the net for CIR-images 12. This shows, that the missing colour 
information results in no more than 30% less classes, which can 
be distinguished. These numerous classes are achieved by using 
structural and texture information. An example for a semantic 
net used for CIR-images, as well as a more detailed description 
regarding the use of semantic nets for interpretation is described 
in (Pakzad, 2001, Pakzad et al. 2001, Heipke et al. 2000). 
6.2 System Overview 
Figure 3 shows an overview of the multitemporal interpretation 
system. The interpretation starts with an initial segmentation 
based on Geo-Data and radiometric/textural information for the 
images of the first epoch, as described above. This results in 
segment borders, which are the basis of further interpretation. 
An interpretation for every segment is performed inside the 
segment borders. 
SS Start | 
  
  
  
  
  
  
  
    
  
  
  
  
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Figure 3. Multitemporal interpretation system 
  
  
The interpretation procedure utilizes semantic nets as 
knowledge base. The semantic net, which is used for the 
interpretation of the first epoch differs from the semantic nets 
for interpretation of the next epochs. The reason is that some 
classes can only be recognized by using temporal history. This 
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