Full text: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
127 
represented in this window. With time, the image analysts gain 
more experience in image fusion and are therefore capable of 
introducing better start values for the parameters in the process. 
Similar types of areas and datasets require similar values for a 
successful merge. Crucial in the overall achievement of image 
fusion is the adjustment of the colours in the final product. The 
same product can look very different depending on the 
histogram stretch and assignment of colour in an RGB display. 
The use of filters for noise reduction or edge enhancement is a 
sensitive matter in visual image interpretation. Depending on 
the scale and type of feature to be looked at, filters can help 
understand the image. In some cases however, it leads to a loss 
of detail which might be relevant for the application performed. 
It has to be decided on a case by case basis, if and when to 
apply filtering to VIR or SAR data. 
Further research is necessary in the development of help tools to 
support the understanding of the content of fused products 
along with quality measures. Future efforts in image fusion at 
the WEUSC will be devoted to more standardization in order to 
streamline the production of fused images for certain 
applications. Currently, many techniques and image 
combinations are used following a trial and error approach. The 
experiences of the group of image analysts are vital to the 
definition of development strategies of the data fusion system. 
ACKNOWLEDGEMENTS 
The authors kindly acknowledge the contribution of the 
WEUSC staff, in particular the image analysts, to this article. 
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