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In remotely sensed hyperspectral imagery, many samples are collected on a given flight and many variable factors contribute to the distribution of samples. Various factors transform spectral responses causing them to appear differently in different contexts. We develop a method that infers context via spectra population distribution analysis. In this manner, feature space orientations of sets of spectral signatures are characterized using random set models. The models allow for the characterization of complex and irregular patterns in a feature space. The developed random set framework for context-based classification applies context-specific classifiers in an ensemblelike manner, and aggregates their decisions based on their contextual relevance to the spectra under test. Results indicate that the proposed method improves classification accuracy over similar classifiers, which make no use of contextual information, and performs well when compared to similar context-based approaches.