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Efficient real-time discrimination of image objects is greatly affected by their radiometry, which is only partly accounted for by image scene calibration. Such calibration treats mainly variations in flux density in the generalized imaged scene plane rather than on the objects' surface. The proposed methodology uses ratios between secondary parameterizations: e.g., absorption features and spectral derivatives. Clustering in the ratios' parameter space may allow differentiation between image objects despite limitations regarding their relative calibration. The usefulness of this approach was demonstrated in the challenging task of separating Mediterranean vegetation species using imaging spectroscopy.