Iterative possibility distributions refining in pixel-based images classification framework
Résumé
In this study, an incremental and iterative approach for possibility distributions estimation in pixel-based images classification context is proposed. This approach is based on the use of possibilistic reasoning in order to enrich a set of samples serving for the initial estimation of possibility distributions. The use of possibilistic concepts enables an important flexibility for the integration of a context-based additional semantic knowledge source formed by pixels belonging with high certainty to different semantic classes (called possibilistic seeds), into the available knowledge encoded by possibility distributions. Once possibilistic seeds are extracted, possibility distributions are incrementally updated and refined. Synthetic images composed of two thematic classes are generated in order to evaluate the performances of the proposed approach. Initial possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability distributions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. The possibilistic seeds extraction process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure.