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- Olivier Eches, Nicolas Dobigeon, Corinne Mailhes, Jean-Yves Tourneret
- IEEE Transactions on Image Processing
- 2010

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)

- Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
- IEEE Trans. Geoscience and Remote Sensing
- 2011

This paper describes a new algorithm for hyperspectral 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)

- O. Eches, N. Dobigeon, C. Mailhes, J.-Y. Tourneret
- 2009 IEEE/SP 15th Workshop on Statistical Signal…
- 2009

This paper studies a new unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of endmembers which are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, endmembers are modeled as Gaussian vectors with known means (resulting from an endmember extraction algorithm… (More)

- Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
- IEEE Journal of Selected Topics in Signal…
- 2010

This paper studies a semi-supervised Bayesian unmixing algorithm for hyperspectral images. This algorithm is based on the normal compositional model recently introduced 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 <i>endmembers</i>.… (More)

- Olivier Eches, Jon Atli Benediktsson, Nicolas Dobigeon, Jean-Yves Tourneret
- IEEE Transactions on Image Processing
- 2013

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)

- Olivier Eches, Mireille Guillaume
- IEEE Geosci. Remote Sensing Lett.
- 2014

- Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
- 2010 2nd Workshop on Hyperspectral Image and…
- 2010

This paper studies a new Bayesian algorithm for the unmixing of hyperspectral images. The proposed Bayesian algorithm is based on the well-known linear mixing model (LMM). Spatial correlations between pixels are introduced using hidden variables, or labels, and modeled via a Potts-Markov random field. We assume that the pure materials (or endmembers)… (More)

- Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret, Hichem Snoussi
- 2011 IEEE International Conference on Acoustics…
- 2011

This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear combination of endmembers whose corresponding fractions or abundances are estimated by a Bayesian algorithm. This approach requires to define prior distributions for the parameters of… (More)

- Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
- 2009 First Workshop on Hyperspectral Image and…
- 2009

This paper studies a new Bayesian algorithm to unmix hyperspectral images. The algorithm is based on the recent normal compositional model introduced by Eismann. Contrary to the standard linear mixing model, the endmember spectra are assumed to be random signatures with know mean vectors. Appropriate prior distributions are assigned to the abundance… (More)

- Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
- 2010 IEEE International Conference on Acoustics…
- 2010

In this paper, we address the problem of unmixing hyperspectral images in a semi-supervised framework using the normal compositional model recently introduced by Eismann and Stein. Each pixel of the image is modeled as a linear combination of random endmembers. More precisely, endmembers are modeled as Gaussian vectors whose means belong to a known spectral… (More)