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Communication Dans Un Congrès Année : 2016

SVM classifier fusion using belief functions: application to hyperspectral data classification

Résumé

Hyperspectral imagery is a powerful source of information for recognition problems in a variety of fields. However, the resulting data volume is a challenge for classification methods especially considering industrial context requirements. Support Vector Machines (SVMs), commonly used classifiers for hyperspectral data, are originally suited for binary problems. Basing our study on [12] bbas allocation for binary classifiers, we investigate different strategies to combine two-class SVMs and tackle the multiclass problem. We evaluate the use of belief functions regarding the matter of SVM fusion with hyperspectral data for a waste sorting industrial application. We specifically highlight two possible ways of building a fast multi-class classifier using the belief functions framework that takes into account the process uncertainties and can use different information sources such as complementary spectra features.
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Dates et versions

hal-01691958 , version 1 (24-01-2018)

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Marie Lachaize, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, Roger Reynaud. SVM classifier fusion using belief functions: application to hyperspectral data classification. Belief Functions: Theory and Applications - 4th International Conference, Sep 2016, Prague, Czech Republic. ⟨10.1007/978-3-319-45559-4_12⟩. ⟨hal-01691958⟩
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