Multi-source SVM fusion for environmental monitoring in Marquesas archipelago

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.