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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
for all partitions 
selecting the data points 
if the set is not empty 
reading pixel coordinates 
for all ordering epochs 
calculating learning rate and d(x) 
for all data points 
for all neurons 
calculating distance 
endfor 
for all neurons 
if in calculated neighborhood 
modifying weights 
endif 
endfor 
endfor 
endfor 
endif 
endfor 
The above algorithm has strong nested loop structure. The 
method is therefore computation intensive and sensitive for the 
loop control parameters. Fixing the number of epochs, neurons, 
partition size, the number of data points has also great influence 
on the performance. It was observed, that the smaller sized 
partitions resulted faster runs, than bigger partitions, which is 
not unexpected looking at the above algorithm. 
The function d(x) is the permanently decreasing neighborhood. 
The result of the image analysis can be seen in Figure 6, which 
shows the regular partitions, too. The structuring elements have 
detected visually most of the road segments and their junctions, 
only some small errors were occurred. These errors must be 
eliminated by implementing constrains for the final neuron 
graph structure during the processing, which is the topic of the 
current research. 
5. CONCLUSION 
The newly developed self organizing neuron graph is a flexible 
tool in analyzing different types of remotely sensed images. The 
template matching problem was solved by a given fiducial mark 
graph. Because the camera manufacturers have own figures for 
fiducials, the SONG algorithm is capable to fit not only a single 
neuron graph to the assumed image part containing the fiducial, 
but a whole series. Measuring the fitness for all matching, the 
highest fitness identifies furthermore the type of camera. In this 
way, the SONG technique recognizes the camera itself. The . 
SONG matching is fast, it can provide an alternative solution in 
the automatic interior orientation. 
The building and road detection belong to the object detection 
approaches, which are the most interesting in the modern digital 
photogrammetry. The building detection, if we have any 
preliminary hypothesis about the building structure, is a 
relational matching task. In this sense, the SONG method can 
help to realize similar solutions, which are mostly related to the 
relational matching. The shown example with the Pentagon 
building can be generalized: if one can describe the structure of 
a building in form of a graph, it can be found in an image using 
the developed self-organizing technique; furthermore this 
building can be "traced" in an aerial image strip or block. Only 
the same preprocessing operators (e.g. classifiers) must be 
executed, prior the SONG run. 
The other presented building detection experiment proved that 
this method can be used to detect the structure of an unknown 
building by creating a hypothesis neuron graph and testing its 
suitability. The given example has shown that this hypothesis 
testing can be a way to improve the current version of the 
algorithm. The test was an alternative solution for getting the 
skeleton of an object, solved by the application of artificial 
intelligence instead of the known classical skeleton operator. 
The most interesting test was the road detection. In this situation 
a black-and-white orthoimage ensured the necessary data points 
by simple thresholding, which is a very fast image processing 
technique. After the segmentation, the SONG algorithm has 
found the rough road structure. This can be interpreted in two 
Ways. 
Once these road segments are objects to perform further 
grouping methods to obtain real road network. In this way the 
creation of a classical GIS-type topology, then a follow-up 
topology analysis and restructuring can lead to the network. 
The other possible application of the obtained results is their 
interpretation as first approximation of the road network. Using 
this philosophy, the connecting processing steps can be 
buffering the graph edges. With the fusion of the independent 
buffers, we will get a subsample of the image, where the 
probability of the roads in the image is relatively high. 
Thereafter the different checking algorithms can be executed, 
which test these image subsamples on containing roads. If the 
test is positive, the exact road segment and its geometric 
position can be detected using more accurate methods. In this 
checking the SONG technique can be involved in the complex 
algorithm, i.e. with also the tuning phase. 
The result image of the road detection contains some noisy 
parts, especially in urban areas. The method should be used with 
a preliminary masked input image, which doesn't have any 
urban land covers. Other alternative would be to establish an 
urban parameter set, which can have better performance under 
built-in circumstances. 
The last test points to a new possibility for using highly 
parallelized algorithms in photogrammetric image analysis, 
because after partitioning the input image, the same steps of the 
algorithm must be evaluated for each image parts. If a multi 
processor computing environment (e.g. dual Pentium PC, or 
even computer cluster or grid) is available, the method can be 
implemented and used. 
The general assumption of the SONG technique is to own an 
adequate initial neuron graph. A nice initialization could be the 
use of an obsolete topographic map, where the novel method 
would be responsible to check the old map (database) content to 
the new image information. In this meaning the method suits 
also to the map update procedures. : 
The road detection test lasted in Matlab (interpreter type) 
environment about two minutes on an Intel P4 1.7 GHz 
machine, all the other are significantly faster thanks to the 
compiler (MS Visual C++) realization. The image size was 
555 x 827 pixel. This fact also underlines the performance 
power of the method. 
As the paper presented, the generalization of the original 
Kohonen-type SOM can be extended by graphs. The newly 
developed SONG method has been proved its capability in 
different photogrammetric and remote sensing tasks. The 
technique has shown how to cope with different types of tasks 
using the same algorithmic background. The method is 
important in the point of view of the artificial intelligence and 
neural networks, because the general suitability and 
applicability has also been proved. 
ACKNOWLEDGEMENTS 
The author expresses his thanks to the Alexander von Humboldt 
Foundation, when the work was started; to the Institute of 
Photogrammetry and Geolnformation, Hannover; to the 
Hungarian Higher Education Research Program (FKFP) for 
partly financing the research. 
   
    
  
   
  
    
  
  
  
  
  
  
  
  
  
   
  
  
  
  
  
  
   
   
  
   
    
   
   
  
  
  
  
  
  
  
  
  
  
  
  
  
   
   
  
  
   
  
   
   
  
  
  
  
  
   
  
  
  
  
   
  
   
   
  
  
  
  
  
  
  
  
  
   
   
  
  
  
  
  
   
   
  
  
    
  
  
  
  
   
   
  
   
   
    
  
   
  
   
  
   
   
    
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