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Target detection in hyperspectral imagery is the process of locating pixelsfrom an image which are likely to contain target, typically done by comparingone or more spectra for the desired target material to each pixel in the image.Target identification is the process of target detection incorporating anadditional process to identify more specifically the material that is presentin each pixel that scored high in detection. Detection is generally a 2-classproblem of target vs. background, and identification is a many class problemincluding target, background, and additional know materials. The identificationprocess we present is probabilistic and hierarchical which providestransparency to the process and produces trustworthy output. In this paper weshow that target identification has a much lower false alarm rate thandetection alone, and provide a detailed explanation of a robust identificationmethod using probabilistic hierarchical classification that handles the vaguecategories of materials that depend on users which are different than thespecific physical categories of chemical constituents. Identification is oftendone by comparing mixtures of materials including the target spectra tomixtures of materials that do not include the target spectra, possibly withother steps. (band combinations, feature checking, background removal, etc.)Standard linear regression does not handle these problems well because thenumber of regressors (identification spectra) is greater than the number offeature variables (bands), and there are multiple correlated spectra. Ourproposed method handles these challenges efficiently and provides additionalimportant practical information in the form of hierarchical probabilitiescomputed from Bayesian model averaging.

Target Identification and Bayesian Model Averaging with Probabilistic Hierarchical Factor Probabilities