  Hyperspectral data consists of large number of features which require
sophisticated analysis to be extracted. A popular approach to reduce
computational cost, facilitate information representation and accelerate
knowledge discovery is to eliminate bands that do not improve the
classification and analysis methods being applied. In particular, algorithms
that perform band elimination should be designed to take advantage of the
specifics of the classification method being used. This paper employs a
recently proposed filter-feature-selection algorithm based on minimizing a
tight bound on the VC dimension. We have successfully applied this algorithm to
determine a reasonable subset of bands without any user-defined stopping
criteria on widely used hyperspectral images and demonstrate that this method
outperforms state-of-the-art methods in terms of both sparsity of feature set
as well as accuracy of classification.\end{abstract}
