A Fuzzy-input Fuzzy-output SVM Technique for Classification of Hyperspectral Remote Sensing Images

In this paper we present a novel Fuzzy input - Fuzzy output Support Vector Machine (F2SVM) classifier, which is able to process fuzzy information given as input to the classification algorithm and to produce fuzzy classification outputs. The main novelties of the proposed F2-SVM consist of: i) simultaneous and proper management of both uncertainty and fuzzy information; ii) capability to model one-to-many relations between a pattern and the related information classes both in the learning and in the classification phases; iii) capability to address multiclass problems in a fuzzy framework. Experimental results obtained on an hyperspectral data set confirm the effectiveness of the proposed technique, which provided classification accuracies higher than those exhibited by a fuzzy multilayer perceptron neural network classifier used for comparisons. © 2006 IEEE.