Biomedical subjects

Glenn Healey

Publications and source records attributed to Glenn Healey.

5 recordsLinked to original sources

PubMed2004

Using independent component analysis for material estimation in hyperspectral images.

We develop a method for automated material estimation in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of unknown materials. The method is particularly useful for applications in which material regions in a scene are smaller than one pixel. In contrast to many material estimation methods, the new method uses the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We demonstrate properties of the method by application to airborne hyperspectral data.

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PubMed2004

Estimating visible through near-infrared spectral reflectance from a sensor radiance spectrum.

We show that surface spectral reflectance can be separated from illumination effects in visible through near-infrared (350 nm-1740 nm) hyperspectral data by using only the information in a single radiance spectrum. The separation method exploits the fact that reflectance and illumination spectra typically lie in distinct subspaces. We present a comparison of a linear and a nonlinear algorithm for the separation. These algorithms compute an estimate of the spectral reflectance up to a scaling factor. In addition, we present an iterative method that is used to determine the starting point for the nonlinear algorithm. We also develop a method for selecting the dimension of the reflectance and illumination subspaces that is appropriate for material identification applications. The accuracy of the separation methods is quantified by application to noisy visible through near-infrared spectral data with a database of 107 materials and 3000 illumination spectra. The utility of the separation method for material identification is demonstrated with the same database. The results show that accurate reflectance recovery and material identification is possible by use of visible through near-infrared spectral data over the outdoor environmental conditions represented in this data set.

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PubMed2003

Global spectral irradiance variability and material discrimination at Boulder, Colorado.

We analyze 7,258 global spectral irradiance functions over 0.4-2.2 microm that were acquired over a wide range of conditions at Boulder, Colorado, during the summer of 1997. We show that low-dimensional linear models can be used to capture the variability in these spectra over both the visible and the 0.4-2.2 microm spectral ranges. Using a linear model, we compare the Boulder data with the previous study of Judd et al. [J. Opt. Soc. Am. 54, 1031 (1964)] over the visible wavelengths. We also examine the agreement of the Boulder data with a spectral database generated by using the MODTRAN 4.0 radiative transfer code. We use a database of 223 minerals to consider the effect of the spectral variability in the global spectral irradiance functions on hyperspectral material identification. We show that the 223 minerals can be discriminated accurately over the variability in the Boulder data with subspace projection techniques.

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PubMed2002

Invariant identification of material mixtures in airborne spectrometer data.

We present an algorithm for identifying linear mixtures of a specified set of materials in 0.4-2.5 microm airborne imaging spectrometer data. The algorithm is invariant to the illumination and atmospheric conditions and the relative amounts of the specified materials within a pixel. Only the spectral reflectance functions for the specified materials are required by the algorithm. Invariance over illumination and atmospheric conditions is achieved by incorporating a physical model for scene variability in the constrained optimization formulation. The algorithm also computes estimates of the amounts of the specified materials in identified mixtures. We demonstrate the effectiveness of the algorithm by using real and synthetic Hyperspectral Digital Imaging Collection Experiment imagery acquired over a range of conditions and altitudes.

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PubMed2002

Using reflectance models for color scanner calibration.

We examine the use of linear spectral reflectance models for calibrating a color scanner to generate device-independent CIE XYZ values from scanner vectors. Polynomial regression approaches to color scanner calibration use parameterized functions to approximate the calibration mapping over a set of training colors. These approaches can perform poorly if the parameterized functions do not accurately model the structure of the desired calibration mapping. Several studies have shown that linear reflectance models accurately characterize a wide range of materials. By viewing color scanner calibration as reflectance estimation, we can incorporate linear reflectance models into the calibration process. We show that in most cases linear models do not constrain the calibration problem sufficiently to allow exact recovery of X, Y, Z from a scanner vector obtained with three filters. By examining a series of methods that exploit information about reflectance functions, however, we show that reflectance information can be used to improve the accuracy of calibration over that of standard methods applied to the same set of inputs.

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