Robust hyperspectral unmixing accounting for residual components
Résumé
This paper presents a new hyperspectral mixture model jointly with a Bayesian algorithm for supervised hyperspectral unmixing. Based on the residual component analysis model, the proposed formulation assumes the linear model to be corrupted by an additive term that accounts for mismodelling effects (ME). The ME formulation takes into account the effect of outliers, the propagated errors in the signal processing chain and copes with some types of endmember variability (EV) or nonlinearity (NL). The known constraints on the model parameters are modeled via suitable priors. The resulting posterior distribution is optimized using a coordinate descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. The proposed model and estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.
Mots clés
Bayesian inference
belief networks
learning (artificial intelligence)
maximum likelihood estimation
source separation
robust supervised hyperspectral unmixing
Bayesian algorithm
residual component analysis model
mismodelling effects
ME formulation
endmember variability
EV
signal processing chain
posterior distribution
coordinate descent algorithm
maximum a posteriori estimator
source separation problem
Hyperspectral imaging
Signal processing algorithms
Bayes methods
Signal processing
Computational modeling
Inference algorithms
Mixture models
Hyperspectral imagery
robust unmixing
Bayesian estimation
Gaussian process
gamma Markov random field
| Origine | Fichiers produits par l'(les) auteur(s) |
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