In this study, an iterative contextual approach for images classification is proposed. This approach is based on the use of possibilistic reasoning in order to diffuse the possibilistic knowledge. 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. The possibilistic seeds extraction and classification process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure. Once possibilistic seeds are extracted and classified, possibility distributions are updated and refined in order to diffuse the possibilistic knowledge. 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. Synthetic and real images are used in order to evaluate the performances of the proposed approach.