We present in this paper the use of two auto-adaptive information-fusion methods for a satellite image classification problem. These methods come from possibility theory. Several information-fusion methods are available for different kinds of problems. Auto-adaptive fusion allows to have a fusion which modifies its behavior according to information to be merged. It has a conjunctive behavior when sources agree, and it turns to disjunctive behavior when conflict 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 difficulty to find 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 conflict, and is able to turn to a disjunctive fusion as conflict between sources increases. This makes a better classification than a simple conjunctive fusion.
"Autoadaptive information fusion for satellite image classification", Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226864; https://doi.org/10.1117/12.226864