Boosting vs maximum de vraisemblance
2002
Arnaud, Michel | Bailly, Jean-Stéphane | Puech, Christian
This article sets out to demonstrate how boostingcan serve as a supervised classification method and to compare its results with those of conventional methods. The comparison begins with a theoretical example drown from remote sensing classification in which several criteria are varied: number of pixels par class, overlapping (or not) of radiomatric values between classes, with and without spatial structuring of classes within the geographical space. It is seen that 1) maximum likelihood give better results than boosting when the radiometric values for each class are clearly separated. This advantage is lost as the number of pixels par class increases; 2) boosting outperforms maximum likelihood in the event of overlapping radiometric variable classes, wether or not there is a spatial structure.
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