A novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) is introduced in this paper, with feature selection capabilities, for the classification of an IKONOS image. The structure of the proposed network is developed in a sequential fashion using the group method of data handling (GMDH) algorithm. The node models, regarded as generic classifiers, are represented by fuzzy rule-based systems, combined with a fusion scheme. A data splitting mechanism is incorporated to discriminate between correctly classified and ambiguous pixels. The classifier was tested on the wetland of international importance of Lake Koronia, Greece, and the surrounding agricultural area. To achieve higher classification accuracy, the image was decomposed into two zones: the wetland and the agricultural zones. Apart from the initial bands, additional input features were considered: textural features, intensity-hue-saturation (IHS) and tasseled cap transformation. To assess the quality of the suggested model, the SONeFMUC was compared with a maximum likelihood classifier (MLC). The experimental results show that the SONeFMUC exhibited superior performance to the MLC, providing less confusion of the dominant classes in both zones. In the wetland zone, an overall accuracy of 89.5% was attained.
Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece 2:
Laboratory of Remote Sensing and GIS, Faculty of Agronomy, Aristotle University of Thessaloniki, Thessaloniki, Greece 3:
Laboratory of Applied Soil Science, Faculty of Agronomy, Aristotle University of Thessaloniki, Thessaloniki, Greece