Classification of hyperspectral data is challenging because of high dimensionality (O(100)) inputs, several possible output classes with uneven priors, and scarcity of labeled information. In an earlier work, a multiclassifier system arranged as a binary hierarchy was developed to group classes for easier, progressive discrimination [27]. This paper substantially expands the scope of such a system by integrating a feature reduction scheme that adaptively adjusts to the amount of labeled data available, while exploiting the highly correlated nature of certain adjacent hyperspectral bands. The resulting best-basis binary hierarchical classifier (BB-BHC) family is thus able to address the “small sample size” problem, as evidenced by our experimental results.