Linear models for spectral reflectance functions over the mid-wave and long-wave infrared

Healey, Glenn; Benites, Luis

doi:10.1364/josaa.15.002216

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…These approximations represent a given set of reflectance spectra with increasing accuracy as m is increased and have been used to model visible 3 as well as midwave and longwave 16 infrared reflectance spectra. The value of m that is used for the approximation depends on the application.…”

Section: Illumination Invariants 21 Linear Model For Spectral Distrimentioning

confidence: 99%

Modeling distribution changes for hyperspectral image analysis

Kuan

Healey

2007

Opt. Eng

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We use physical considerations to show that an affine transformation can be used to model the effect of environmental changes on hyperspectral image distributions. This allows the generation of a vector of moment invariants that describes an image distribution but does not depend on the environmental conditions. These vectors maintain the invariant property after each image band is spatially filtered which allows the representation to capture spatial properties. We use the distribution invariants and the Fisher discriminant to reduce the size of the representation by selecting optimized spectral bands. We apply the methods developed in this work to the illumination-invariant classification and recognition of regions in airborne images. We also show that the distribution transformation model can be used for change detection in regions viewed under unknown conditions.

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Section: Illumination Invariants 21 Linear Model For Spectral Distrimentioning

confidence: 99%

Modeling distribution changes for hyperspectral image analysis

Kuan

Healey

2007

Opt. Eng

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“…Low-dimensional linear models have been shown to be an accurate representation for visible and infrared spectral-reflectance functions [6], [17], [30], [32], as well as for outdoor illumination spectra [8], [9], [23], [24], [33]- [35], [37], [41]. In this section, we consider the use of linear models for the sets of observed spectral-radiance functions for different materials.…”

Section: Dimensionality Analysismentioning

confidence: 99%

Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions

Healey

Slater

1999

IEEE Trans. Geosci. Remote Sensing

212

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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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“…Recent decades have witnessed a growing interest in the analysis of spectral signals applied to spectral compression and reconstruction. Thus, efficient spectral representation and color communication, together with the spectral analysis of solar illuminations and reflectance of colored samples, have been the subject of extensive research [1][2][3][4][5][6][7]. Within this context, the characteristics of the solar radiation spectrum in the ultraviolet, visible, and near-infrared regions is of crucial consideration in many disciplines, particularly in imaging, vision, and color science [8].…”

Section: Introductionmentioning

confidence: 99%

Spectral recovery of outdoor illumination by an extension of the Bayesian inverse approach to the Gaussian mixture model

et al. 2012

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The Bayesian inference approach to the inverse problem of spectral signal recovery has been extended to mixtures of Gaussian probability distributions of a training dataset in order to increase the efficiency of estimating the spectral signal from the response of a transformation system. Bayesian (BIC) and Akaike (AIC) information criteria were assessed in order to provide the Gaussian mixture model (GMM) with the optimum number of clusters within the spectral space. The spectra of 2600 solar illuminations measured in Granada (Spain) were recovered over the range of 360-830 nm from their corresponding tristimulus values using a linear model of basis functions, the Wiener inverse (WI) method, and the Bayesian inverse approach extended to the GMM (BGMM). A model of Gaussian mixtures for solar irradiance was deemed to be more appropriate than a single Gaussian distribution for representing the probability distribution of the solar spectral data. The results showed that the estimation performance of the BGMM method was better than either the linear model or the WI method for the spectral approximation of daylight from the three-dimensional tristimulus values.

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Linear models for spectral reflectance functions over the mid-wave and long-wave infrared

Cited by 15 publications

References 19 publications

Modeling distribution changes for hyperspectral image analysis

Modeling distribution changes for hyperspectral image analysis

Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions

Spectral recovery of outdoor illumination by an extension of the Bayesian inverse approach to the Gaussian mixture model

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