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 (F 2 -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 F 2 -SVM consist of: i) si...

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Bibliographic Details
Main Authors: Borasca, B., Bruzzone, L., Carlin, L., Zusi, M.
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Fuzzy neural networks
Fuzzy sets
Hyperspectral imaging
Hyperspectral sensors
Multilayer perceptrons
Remote sensing
Support vector machine classification
Support vector machines
Uncertainty
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Summary:In this paper we present a novel fuzzy input-fuzzy output support vector machine (F 2 -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 F 2 -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
DOI:10.1109/NORSIG.2006.275261