Auto-adaptive Information-fusion for Satellite Images Classiication
نویسنده
چکیده
We present in this paper the use of two auto-adaptive information-fusion methods for a satellite image classi-cation problem. These methods come from possibility theory. Several information-fusion methods are available for diierent kinds of problems. Auto-adaptive fusion allows to have a fusion which modiies its behaviour according to information to be merged. It has a conjunctive behaviour when sources agree, and it turns to disjunctive behaviour when connict between sources turns greater. In our image processing application, we have used conjunctive fusions so far because sources usually agree on the choice of a class for a pixel. But when we increase the number of sources, we increase by the same time the diiculty to nd a common choice from all sources about a pixel. So a disjunctive fusion would be much appropriate for this pixel. An auto-adaptive fusion is able to apply a conjunctive fusion for pixels without connict, and is able to turn to a disjunctive fusion as connict between sources increases. This makes a better classiication than a simple conjunctive fusion.
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تاریخ انتشار 2007