Results pointed out that the SVM classifier with RBF kernel was generally the best choice (with accuracy higher
than 90%) among all the configurations compared, and the use ofmultiple bands globally improves classification.
One of the critical elements found in this case study is given by the presence of sand and sandmixed with rocks.
The use of different configurations for the SVMs, i.e. different kernels and values of the setting parameters,
allowed us to calibrate the classifier also to cope with a specific need, as in our case, to achieve a reliable discrimination
of sand from urban area.