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

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.