Abstract
To deal with the arduous task of hyperspectral image classification band selection is a prominent approach broadly used in the literature. Rough set (RS) theory is a paradigm suitable for handling uncertain, incomplete, and vague data. By incorporating various concepts, classical RS has been extended for band selection capable of handling those problems that classical RS cannot deal with. Four forward greedy hyperspectral band selection algorithms are empirically studied in this paper achieved using the rough set, variable precision rough set, tolerance rough set, and neighborhood rough set. The usefulness of these state-of-the-art techniques is gauged in terms of average overall classification accuracy, average kappa accuracy, and standard deviation acquired by using support vector machine classifier on two real hyperspectral data sets.
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