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Nonlinear hyperspectral unmixing using Gaussian processes

Altmann, Yoann and Dobigeon, Nicolas and Tourneret, Jean-Yves and Mclaughlin, Stephen Nonlinear hyperspectral unmixing using Gaussian processes. (2013) In: IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - WHISPERS 2013, 24 June 2013 - 27 June 2013 (Gainesville, United States). (Unpublished)

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This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Gaussian process latent variable model. The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is compared with state-of-the-art unmixing strategies on synthetic data. One of the interesting propertiesof the proposed strategy is its robustness to the absence of pure pixels in the image.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-01135826
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
ANR- 12-BS03-003
Deposited On:26 Mar 2015 07:38

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