We present an algorithm for subpixel material detection in hyperspectral data that is invariant to the illumination and atmospheric conditions. The algorithm does not require atmospheric correction. The target material spectral reflectance is the only required prior information. A target material subspace model is constructed from the reflectance using a physical model and a background subspace model is estimated directly from the image. These two subspace models are used to compute maximum-likelihood estimates (MLEs) for the target material component and the background component at each image pixel. These estimates form the basis of a generalized likelihood ratio test for subpixel material detection. We present experimental results, using Hyperspectral Digital Imagery Collection Experiment (HYDICE) imagery, that demonstrate the utility of the algorithm for subpixel material detection under varying illumination and atmospheric conditions.

Color textures contain a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination-invariant spatial information from a color image. In this paper, we address the problem of deriving optimal filters for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Similar features have been used in previous studies. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pairwise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filter.

We present an algorithm for subpixel material identification that is invariant to the illumination and atmospheric conditions. The target material spectral reflectance is the only prior information required by the algorithm. A target material subspace model is constructed from the reflectance using a physical model and a background subspace model is estimated directly from the image. These two subspace models are used to compute maximum likelihood estimates for the target material component and the background component at each image pixel. These estimates form the basis of a generalized likelihood ratio test for subpixel material identification. We present experimental results using HYDICE imagery that demonstrate the utility of the algorithm for subpixel material identification under varying illumination and atmospheric conditions.

Color texture contains a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination invariant spatial information from a color image. In this paper, we address the problem of deriving optimal fillers for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pair-wise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filler.

Color textures contain a large amount of spectral and spatial structure which can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination-invariant spatial information from a color image. In this paper, we address the problem of designing optimal filters for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for applying the pairwise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filter.

A new class of multiscale symmetry features provides a useful high-level representation for color texture. These symmetry features are defined within and between the bands of a color image using complex moments computed from the output of a bank of orientation and scale selective filters. We show that these features not only represent symmetry information but are also invariant to rotation, scale, and illumination conditions. The features computed between color bands are motivated by opponent process mechanisms in human vision. Experimental results are provided to show the performance of this set of features for texture classification and retrieval.

A new class of multiscale symmetry features provides a useful high level representation for color texture. These symmetry features are defined within and between color bands using complex moments computed from the output of a bank of orientation and scale selective filters. From this representation, wee derive a set of features which are invariant to rotation, scale, and illumination conditions. Experimental results are provided to show the performance of this set of features for texture classification and retrieval.