OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Bioucas-Dias, José M. and Plaza, Antonio and Dobigeon, Nicolas and Parente, Mario and Du, Qian and Gader, Paul and Chanussot, Jocelyn Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches. (2012) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5 (n° 2). pp. 354-379. ISSN 1939-1404

[img](Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2188Kb

Official URL: http://dx.doi.org/10.1109/JSTARS.2012.2194696

Abstract

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, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

Item Type:Article
Additional Information:Thanks to IEEE editor. The original publication is available at http://ieeexplore.ieee.org
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Other partners > Institut National Polytechnique de Grenoble (FRANCE)
Other partners > Mississipi State University - MSU (USA)
Other partners > University of Massachusetts Amherst - UMASS AMHERST (USA)
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
Other partners > Universidade Técnica de Lisboa - UTL (PORTUGAL)
Other partners > Universidad de Extremadura Uex (SPAIN)
Other partners > University of Florida (USA)
Other partners > Université Joseph Fourier Grenoble 1 - UJF (FRANCE)
Other partners > Université Stendhal-Grenoble 3 - U3 (FRANCE)
Laboratory name:
Statistics:download
Deposited By:Nicolas DOBIGEON

Repository Staff Only: item control page