Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation. Reduction can be done by either identifying bands from the original subset (selection), or by employing various transforms that produce new features (extraction). Research has noted challenges in both directions. In feature selection, identifying an "ideal" spectral band subset is a hard problem as the number of bands is increasingly large, rendering any exhaustive search unfeasible. To counter this, various approaches have been proposed that combine a search algorithm with a criterion function. However, the main drawback of feature selection remains the rather narrow bandwidths covered by the selected bands resulting in possible information loss. In feature extraction, some of the most popular techniques include Principal Component Analysis, Independent Component Analysis, Orthogonal Subspace Projection, etc. While they have been used with success in some instances, the resulting bands lack a physical relationship to the data and are mostly produced using statistical strategies. We propose a new technique for feature reduction that exploits search strategies for feature selection to extract a set of spectral bands from a given imagery. The search strategy uses dynamic programming techniques to identify 'the best set" of features.