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A fuzzy-input fuzzy-output SVM technique for classification of hyperspectral remote sensing images

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4 Author(s)
Borasca, B. ; Dept. of Inf. & Commun. Technol., Univ. of Trento ; Bruzzone, L. ; Carlin, L. ; Zusi, M.

In this paper we present a novel fuzzy input-fuzzy output support vector machine (F2-SVM) 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 a 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

Published in:

Signal Processing Symposium, 2006. NORSIG 2006. Proceedings of the 7th Nordic

Date of Conference:

7-9 June 2006