Résumé
Mapping plant species in montane tropical ecosystems needs the use of complementary information sources to be optimally accurate. In this paper, we study SVM fusion as a tool to classify several sources as optical, synthetic aperture radar and topographical ones. Our fusion scheme consists first in applying a single SVM on each individual data. Their outputs are then used for a SVM-based decision fusion to predict the final class membership of each sample. SVM fusion outperforms all mono-source SVM, our fusion method showing numerous successful traits.