Empirical study of neighbourhood rough sets based band selection techniques for classification of hyperspectral images

Hyperspectral images are acquired in hundreds of spectral channels that contain rich spectral information of different land-cover objects but at the same time they have high dimensionality with ample redundancy which rises curse of dimensionality and computational issues for classification. To mitigate such issues, band selection that reduces the dimensionality of hyperspectral data is a well-known approach widely used in the literature. Neighbourhood rough set, a variant of rough set capable of analysing continuous values, is a robust mathematical tool for handling uncertain and vague data. In this study, the authors have presented an empirical study of four forward greedy hyperspectral band selection algorithms implemented using the neighbourhood rough set, the variable precision neighbourhood rough set, the consistency measure of neighbourhood rough set and the granulation knowledge-based neighbourhood rough set. The effectiveness of these techniques is compared in terms of average classification accuracy, kappa accuracy and standard deviation obtained by using support vector machine classifier on three real hyperspectral data sets. From the experiments, it is found that the variable precision neighbourhood rough set and the consistency measure of neighbourhood rough set are more robust for selecting informative bands compared to the others. The effectiveness of these techniques is also validated by comparing with some state-of-the-art techniques.