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In this paper, we develop a novel approach to object-material identification in spectral imaging by combining the use of invariant spectral absorption features and statistical machine-learning techniques. Our method hinges on the relevance of spectral absorption features for material identification and casts the problem into a pattern-recognition setting by making use of an invariant representation of the most discriminant band segments in the spectra. Thus, here, we view the identification problem as a classification task, which is effected based upon those invariant absorption segments in the spectra which are most discriminative between the materials under study. To robustly recover those bands that are most relevant to the identification process, we make use of discriminant learning. To illustrate the utility of our method for purposes of material identification, we perform experiments on both terrestrial and remotely sensed hyperspectral imaging data and compare our results to those yielded by an alternative.