A fuzzy neural network model for the estimation of sub-pixel land cover composition

This paper reports on an experimental study designed for the in-depth investigation of how a supervised neuro-fuzzy classifier evaluates partial membership in land cover classes. The system is based on the Fuzzy Multilayer Perceptron model proposed by Pal and Mitra to which modifications in distance measures adopted for computing gradual membership to fuzzy class ate introduced. During the training phase supervised learning is used to assign output class membership to pure training vectors (full membership to one land cover class); the model supports a procedure to automatically compute fuzzy output membership values for mixed Graining pixels. The classifier has been evaluated by conducting two experiments. The first employed simulated test images which include pure and mixed pixels of known geometry and radiometry. The second experiment was conducted on a highly complex real scene of the Venice lagoon (Italy) where water and wetland merge into one another, at sub-pixel level. Accuracy of the results produced by the classifier was evaluated and compared using evaluation tools specifically defined and implemented to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. Results obtained demonstrated in the specific context of mixed pixels that the classification benefits from the integration of neural and fuzzy techniques.