Accuracy of land cover classification is generally improved by inputting multi-sensory and GIS data since complex vegetation type identification benefits from synergism of complementary information. However, multi-source fusion can also deteriorate accuracy when some classes do not benefit from all sources. On the basis of this premise, we introduce a Selective Fusion (SELF) scheme based on Support Vector Machines (SVM) which use a single source for source-specific classes and fuse all sources for classes considered as “in difficulty”. Our method yields better overall accuracy and Kappa than the classical systematic approach since it takes advantage of the accuracy achieved by SVM and its ability to weight numerous and heterogeneous sources without the drawback of being sensible to irrelevant data for source-specific classes. This operational method can be used efficiently to enhance accuracy when analyzing the wealth of information available from remote sensing products.