SVM with multiple polygons - it is more fun together

SVM is a classic supervised classifier, but as with all such techniques the output depends on the training set provided. My initial OTB-Qgis SVM used each polygon in a shapefile as a separate training sample and was not ideal for providing extensive training data. The access to vector layer attributes via otb::Datanode makes it feasible to assign multiple polygons the same class value, there is always the fallback to 1 polygon per class if there are no attributes with the polygons. Here are some comparisons with 4 polygons and the results by using 1 polygon per class and all polygons with the same attribute being assigned to 1 class. The 1 polygon per class allows us to distinguish cultivated vegetation vs natural riparian vegetation and 2 types of rocky/bare areas. While the multiple polygons per class aggregates the vegetated areas and bare areas into 2 classes.