Remote sensing hyperspectral sensors are important and powerful instruments for addressing land-cover classification
problems, as they permit a detailed characterization of the spectral behavior of the considered information classes.
However, the processing of hyperspectral data is particularly complex both from the theoretical viewpoint (e.g. problems
related to the Hughes phenomenon ) and from the computational perspective. In this context, despite many
investigations have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only
few studies analyzed the role of the spectral resolution on the classification accuracy in different application domains. In
this paper, we present an empirical study aimed at understanding the relationships among spectral resolution, classifier
complexity, and classification accuracy obtained with hyperspectral sensors in classification of forest areas. On the basis
of this study, important conclusions can be derived on the choice of the spectral resolution of hyperspectral sensors for
forest applications, also in relation to the complexity of the adopted classification methodology. These conclusions can
be exploited both in the context of the design of hyperspectral sensors (or for programming spectral channels of the
available instruments) and in the phase of development of classification system for hyperspectral data.