A Modified Non-Negative LMS Algorithm and its Stochastic Behavior Analysis
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
non-negativity
adaptive filtering
feature extraction
learning (artificial intelligence)
least mean squares methods
modified nonnegative LMS algorithm
stochastic behavior analysis
hyperspectral image
spectral components
nonlinear hyperspectral unmixing problem
Mathematical model
Equations
kernel-based learning theory
end member components
feature space
nonlinear interaction
synthetic images
real images
Convergence
Signal processing algorithms
Vectors
Stochastic processes
Approximation methods
| Origine | Fichiers produits par l'(les) auteur(s) |
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