Feature-selection for remote-sensing data classification

A great amount of parameters can be derived from the original bands of multispectral remotely-sensed images. In particular, for classification purposes it is important to select which of these parameters allow the classes of interest to be well separated in the feature space. In fact, both classification accuracy and computational efficiency rely on the set of features used. Unfoltunately, as spectral responses are strongly influenced by various environmental factors (e.g., atmosphere interferences and non- homogeneous sunshine distribution) the derived parameters depend not only on the considered classes but also on the peculiar characteristics of analyzed images. Even if many studies have been carried out both to identify more stable parameters and to correct images, the problem is still open. It cannot be a-priori solved on the basis of the only ground classes considered, but an ad-hoc selection is required for each image to be classified. In literature, several feature-selection cr...