1. Hyperspectral image unmixing using manifold learning: methods derivations and comparative tests
- Author
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Cedric Richard, Celine Theys, Nguyen Hoang Nguyen, Paul Honeine, Joseph Louis LAGRANGE (LAGRANGE), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Endmember ,Geodesic ,Equations ,Hyperspectral imaging ,dimensionality-reduction step ,0211 other engineering and technologies ,02 engineering and technology ,spectral component mixture ,abundance estimation step ,nonlinear estimation ,Mathematical model ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,0202 electrical engineering, electronic engineering, information engineering ,linear mixture model ,differential geometry ,Manifolds ,Materials ,geodesic distance ,021101 geological & geomatics engineering ,Mathematics ,Abundance estimation ,Pixel ,business.industry ,Nonlinear dimensionality reduction ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,020206 networking & telecommunications ,Pattern recognition ,hyperspectral image unmixing analysis ,Mixture model ,nonlinear technique ,Nonlinear system ,endmember extraction ,geophysical image processing ,manifold learning method ,comparative testing ,Computer Science::Computer Vision and Pattern Recognition ,learning (artificial intelligence) ,Artificial intelligence ,business ,Signal processing algorithms ,Estimation ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
International audience; In hyperspectral image analysis, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the mostly studied case, nonlinear techniques have been proposed to overcome its limitations. In this paper, a manifold learning approach is used as a dimensionality-reduction step to deal with non-linearities beforehand, or is integrated directly in the endmember extraction and abundance estimation steps using geodesic distances. Simulation results show that these methods improve the precision of estimation in severely nonlinear cases.
- Published
- 2012
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