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We introduce a representation for color texture using unichrome and opponent features computed from Gabor filter outputs. The unichrome features are computed from the spectral bands independently while the opponent features combine information across different spectral bands at different scales. Opponent features are motivated by color opponent mechanisms in human vision. We present a method for efficiently implementing these filters, which is of particular interest for processing the additional information present in color images. Using a data base of 2560 image regions, we show that the multiscale approach using opponent features provides better recognition accuracy than other approaches.

Abstract-The spectral radiance measured by an airborne imaging spectrometer for a material on the Earth's surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limited the success of material-identification algorithms that rely on hyperspectral image data without associated ground-truth information. In this paper, we use a comprehensive physical model to show that the set of observed 0.4-2.5 m spectral-radiance vectors for a material lies in a low-dimensional subspace of the hyperspectral-measurement space. The physical model captures the dependence of the reflected sunlight, reflected skylight, and path-radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, we develop a local maximum-likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. The algorithm requires only the spectral reflectance of the target material as input. We show that the low dimensionality of material subspaces allows for the robust discrimination of a large number of materials over a wide range of conditions. We demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions.

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