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67
Vertex component analysis: A fast algorithm to unmix hyperspectral data
 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 2005
"... Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for ..."
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Cited by 193 (16 self)
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Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: 1) the endmembers are the vertices of a simplex and 2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with stateoftheart methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Hyperspectral unmixing overview: Geometrical, statistical, and sparse regressionbased approaches
 IEEE J. SEL. TOPICS APPL. EARTH OBSERV. REMOTE SENS
, 2012
"... Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher sp ..."
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Cited by 103 (34 self)
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Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, illposed
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
"... Abstract—This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown e ..."
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Cited by 67 (29 self)
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Abstract—This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and fulladditivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images. Index Terms—Bayesian inference, endmember extraction, hyperspectral imagery, linear spectral unmixing, MCMC methods. I.
Does independent component analysis play a role in unmixing hyperspectral data
 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 2005
"... Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) ..."
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Cited by 61 (13 self)
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Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signaltonoise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.
Semisupervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery
, 2008
"... This paper proposes a hierarchical Bayesian model that can be used for semisupervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing ..."
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Cited by 50 (25 self)
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This paper proposes a hierarchical Bayesian model that can be used for semisupervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data.
The Thermal Emission Imaging System (THEMIS) for the Mars 2001 Odyssey mission, Space Sci
 Rev
, 2004
"... Abstract. The Thermal Emission Imaging System (THEMIS) on 2001 Mars Odyssey will investigate the surface mineralogy and physical properties of Mars using multispectral thermalinfrared images in nine wavelengths centered from 6.8 to 14.9 µm, and visible/nearinfrared images in five bands centered ..."
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Cited by 50 (4 self)
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Abstract. The Thermal Emission Imaging System (THEMIS) on 2001 Mars Odyssey will investigate the surface mineralogy and physical properties of Mars using multispectral thermalinfrared images in nine wavelengths centered from 6.8 to 14.9 µm, and visible/nearinfrared images in five bands centered from 0.42 to 0.86 µm. THEMIS will map the entire planet in both day and night multispectral infrared images at 100m per pixel resolution, 60 % of the planet in oneband visible images at 18m per pixel, and several percent of the planet in 5band visible color. Most geologic materials, including carbonates, silicates, sulfates, phosphates, and hydroxides have strong fundamental vibrational absorption bands in the thermalinfrared spectral region that provide diagnostic information on mineral composition. The ability to identify a wide range of minerals allows key aqueous minerals, such as carbonates and hydrothermal silica, to be placed into their proper geologic context. The specific objectives of this investigation are to: (1) determine the mineralogy and petrology of localized deposits associated with hydrothermal or subaqueous environments, and to identify future landing sites likely to represent these environments; (2) search for thermal anomalies associated with active subsurface hydrothermal systems; (3) study smallscale geologic processes and landing
Nonlinear unmixing of hyperspectral images using a generalized bilinear model
 IEEE Trans. Geosci. and Remote Sensing
"... Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sumtoon ..."
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Cited by 41 (21 self)
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Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sumtoone constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data. Index Terms — hyperspectral imagery, spectral unmixing, bilinear model, Bayesian inference, MCMC methods, gradient descent algorithm, least square algorithm. 1.
Hyperspectral Remote Sensing Data Analysis and Future Challenges
"... Abstract—Hyperspectral remote sensing ..."
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Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery
, 2012
"... This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial func ..."
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Cited by 24 (13 self)
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This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial functions leading to a polynomial postnonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model.The performance of the unmixing strategies is evaluated by simulations conducted on synthetic and real data.
Miniature Thermal Emission Spectrometer for the Mars Exploration
 Rovers, J. Geophys. Res
, 2003
"... The Miniature Thermal Emission Spectrometer (MiniTES) will provide remote measurements of mineralogy and thermophysical properties of the scene surrounding the Mars Exploration Rovers, and guide the Rovers to key targets for detailed in situ measurements by other Rover experiments. The specific sci ..."
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Cited by 23 (3 self)
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The Miniature Thermal Emission Spectrometer (MiniTES) will provide remote measurements of mineralogy and thermophysical properties of the scene surrounding the Mars Exploration Rovers, and guide the Rovers to key targets for detailed in situ measurements by other Rover experiments. The specific scientific objectives of the MiniTES investigation are to: (1) determine the mineralogy of rocks and soils; (2) determine the thermophysical properties of selected soil patches; and (3) determine the temperature profile, dust and waterice opacity, and water vapor abundance in the lower atmospheric boundary layer. The MiniTES is a Fourier Transform Spectrometer covering the spectral range 529 µm (339.50 to 1997.06 cm1) with a spectral sample interval of 9.99 cm1. The MiniTES telescope is a 6.35cm diameter Cassegrain telescope that feeds a flatplate Michelson moving mirror mounted on a voicecoil motor assembly. A single deuterated triglycine sulfate (DTGS) uncooled pyroelectric detector with proven space heritage gives a spatial resolution of 20 mrad; an actuated field stop can reduce the field of view to 8 mrad. MiniTES is mounted within the Rover’s Warm