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This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers. These endmembers are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, we model endmembers as Gaussian vectors whose means have been determined using(More)
—This paper describes a new algorithm for hyper-spectral image unmixing. Most unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels. In this paper, a Bayesian model is introduced to exploit these correlations. The image to be unmixed is assumed to be partitioned into regions (or classes)(More)
This paper studies a semi-supervised Bayesian unmixing algorithm for hyperspectral images. This algorithm is based on the normal compositional model recently introcuced by Eismann and Stein. The normal compositional model assumes that each pixel of the image is modeled as a linear combination of an unknown number of pure materials, called endmembers. These(More)
Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using(More)
This paper studies a variational Bayesian unmixing algorithm for hy-perspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear combination of endmem-bers whose corresponding fractions or abundances are estimated by a Bayesian algorithm. This approach requires to define prior distributions for the parameters(More)
In this paper, we address the problem of unmixing hyperspectral images in a semi-supervised framework using the normal composi-tional model recently introduced by Eismann and Stein. Each pixel of the image is modeled as a linear combination of random endmem-bers. More precisely, endmembers are modeled as Gaussian vectors whose means belong to a known(More)
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