A novel kernel-based nonlinear unmixing scheme of hyperspectral images
Résumé
In hyperspectral images, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the most studied case, nonlinear models have been taken into consideration to overcome some limitations of the linear model. In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory. Endmember components at each band are mapped implicitly in a high feature space, in order to address the nonlinear interaction of photons. Experiment results with both synthetic and real images illustrate the effectiveness of the proposed scheme.
Mots clés
learning (artificial intelligence)
geophysical image processing
kernel-based nonlinear unmixing scheme
hyperspectral images
spectral components
Signal processing algorithms
Hyperspectral imaging
Materials
Algorithm design and analysis
Vectors
pixels
linear mixture model
nonlinear hyperspectral unmixing problem
kernel-based learning theory
endmember components
photons
Kernel
| Origine | Fichiers produits par l'(les) auteur(s) |
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