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Communication Dans Un Congrès Année : 2012

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.
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Dates et versions

hal-01966014 , version 1 (27-12-2018)

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Nguyen Hoang Nguyen, Cédric Richard, Paul Honeine, Céline Theys. Hyperspectral image unmixing using manifold learning: methods derivations and comparative tests. Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012, Munich, Germany. pp.3086 - 3089, ⟨10.1109/IGARSS.2012.6350773⟩. ⟨hal-01966014⟩
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