A neuro-fuzzy multilayered classifier for land cover image classification

In this paper, a novel Self-Organizing Neuro-Fuzzy Multilayered Classifier (SONeFMUC) is suggested for land cover classification of a SPOT-5 satellite image. The proposed model is developed in a self-organizing manner by means of the group method of data handling (GMDH) algorithm, exhibiting feature selection capabilities. Each node is regarded as a generic fuzzy neuron classifier (FNC) which is implemented by fuzzy rule-based systems, combined with a decision fusion scheme. A data splitting mechanism is incorporated to discriminate between confident classified and ambiguous pixels, providing an efficient handling of the data flow. The application of the model was performed to the agricultural area of Larisa, Greece. Apart from the initial bands, additional features were used, namely intensity, hue and saturation transformation. The classification performance and the thematic map produced by SONeFMUC demonstrate the classification capabilities of the proposed model.