Figure 8: Example of segmentation errors with different 
neighbourhood systems (left) and the original image (right) 
Results with airborne images 
The methods and approaches presented were taken into account 
for segmentation of airborne images. Images taken by a DMC 
airborne camera (Intergraph GmbH) were radiometrically 
normalised, the model parameters were normalised to a uniform 
length after extraction and a neighbourhood system with 10 
neighbours was used. The segmentation was carried out for 
different texture window sizes and scaling. 
Good results could be achieved with the used pictures when the 
scaling was larger than 50% and the texture windows were 
small (3-5 pixels). Nevertheless, the segmentation is affected if 
there is poor image radiometry (see figure 9, middle picture). 
Moreover, heterogeneous areas, e.g. urban areas with many 
single trees, make segmentation difficult, though in these cases 
a crude discrimination is possible. 
The finer the texture and the higher the scaling the smaller the 
texture window should be chosen. However, no general rules 
can be defined from these results. The texture window size and 
the scaling must be adjusted for every special texture. 
The neighbourhood system, by contrast, has only little 
influence on segmentation results. The reason is that only a few 
parameters contribute to the indication of texture quality. 
Additional parameters (a bigger neighbourhood system) do not 
contain extra information and do not enhance segmentation 
results. A NBS with 10 neighbours is usually adequate, and 
therefore was used for further investigations. 
5. CONCLUSION AND OUTLOOK 
Figure 9. Three examples of segmentation results with aerial 
photos. Blue is the extracted road area. 
The paper considers road detection in panchromatic images for 
traffic observation from airplane platforms. Because of the 
limited image size structure based approaches cannot be applied. 
Therefore, in this paper texture based algorithms are utilized. 
The influence of image quality and pre-processing, image 
scaling, size of the texture window and size of the 
neighbourhood system, as well as the influence of parameter 
normalization where investigated. 
In this context it is interesting to realize that the MRF 
characteristics are independent of illumination of the observed 
area. According to this it is possible to minimize the influence 
of cast shadow - a common problem in natural scenes. 
Altogether, it could be shown that the described method is 
suitable for the distinction of streets and surrounding areas. For 
real-time applications the algorithm and the implementation 
must be optimized. 
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