Hyperspectral Band Selection Based on Rough Set

Band selection is a well-known approach to reduce the dimensionality of hyperspectral imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of the hyperspectral imagery. In this paper, a rough-set-based supervised method is proposed to select informative bands from hyperspectral imagery. The proposed technique exploits rough set theory to compute the relevance and significance of each spectral band. Then, by defining a novel criterion, it selects the informative bands that have higher relevance and significance values. To assess the effectiveness of the proposed band selection technique, three state-of-the-art methods (one supervised and two unsupervised) used in the remote sensing literature are analyzed for comparison on three hyperspectral data sets. The results of this comparison point to the superiority of the proposed technique, especially when a small number of bands are to be selected.