Hyperspectral image unmixing using manifold learning: methods derivations and comparative tests
Résumé
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.
Mots clés
differential geometry
geophysical image processing
learning (artificial intelligence)
nonlinear estimation
hyperspectral image unmixing analysis
manifold learning method
comparative testing
spectral component mixture
linear mixture model
nonlinear technique
dimensionality-reduction step
endmember extraction
abundance estimation step
geodesic distance
Estimation
Manifolds
Signal processing algorithms
Hyperspectral imaging
Equations
Mathematical model
Materials
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