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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2016

Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity or Mismodelling Effects

Résumé

This paper presents three hyperspectral mixture models jointly with Bayesian algorithms for supervised hyperspectral unmixing. Based on the residual component analysis model, the proposed general formulation assumes the linear model to be corrupted by an additive term whose expression can be adapted to account for nonlinearities (NLs), endmember variability (EV), or mismodeling effects (MEs). The NL effect is introduced by considering a polynomial expression that is related to bilinear models. The proposed new formulation of EV accounts for shape and scale endmember changes while enforcing a smooth spectral/spatial variation. The ME formulation considers the effect of outliers and copes with some types of EV and NL. The known constraints on the parameter of each observation model are modeled via suitable priors. The posterior distribution associated with each Bayesian model is optimized using a coordinate descent algorithm, which allows the computation of the maximum a posteriori estimator of the unknown model parameters. The proposed mixture and Bayesian models and their estimation algorithms are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity, when compared with the state-of-the-art algorithms.
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Dates et versions

hal-01965047 , version 1 (04-01-2019)

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  • HAL Id : hal-01965047 , version 1

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Abderrahim Halimi, Paul Honeine, José Bioucas-Dias. Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity or Mismodelling Effects. IEEE Transactions on Image Processing, 2016, 25 (10), pp.4565 - 4579. ⟨hal-01965047⟩
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