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Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

Altmann, Yoann and Dobigeon, Nicolas and Tourneret, Jean-Yves and McLaughlin, Steve Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. (2014) In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 4 May 2014 - 9 May 2014 (Florence, Italy).

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Official URL: http://dx.doi.org/10.1109/ICASSP.2014.6854184


This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

Item Type:Conference or Workshop Item (Paper)
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université de Toulouse I-Sciences Sociales - UT1 (FRANCE)
Other partners > Heriot-Watt University (UNITED KINGDOM)
Laboratory name:
Deposited By: IRIT IRIT
Deposited On:18 Feb 2015 08:37

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