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In this paper the Ordered Weighted Averaging OW A operator is introduced related to data cooperation in the field of image analysis. The concept of OW A operator was introduced by Yager in [5], [6] and [7] as a way for providing aggregations which lie between Max and Min. The structure of these operator involves a type of nonlinearity in the form of an ordering operation on the elements to be aggregated. The main difficulty in using this type of operators is to find the appropriate weighted vector. In our approach, we propose to generate the weights by a Neural Network. Furthermore, depending on the dispersion of sources opinions, the most appropriate operator is used taking into account the agreement or the conflict among the sources. In the following we review some basic ideas of the OW A aggregation operator, we then describe the general system operating showing how the coherence degree among the sources are integrated to produce the adaptative operator to any given situation. The method is tested on Landsat multispectral images, using different supervised and non-supervised classification techniques.
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