A rough set based band selection technique for the analysis of hyperspectral images

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 hyperspectral images. In this paper, a rough set based supervised method is proposed to select informative bands in hyperspectral images. The proposed technique exploits rough set theory to define a novel criterion for selecting informative bands. The performances of the proposed approach were compared with those of three state-of-the-art methods on a hyperspectral data set. Experimental results show the effectiveness of the proposed technique.