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

Abstract

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 (NL), endmember variability (EV), or mismodelling effects (ME). 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 takes into account 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 to the state-of-the-art algorithms.

References in corpus (3)

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• A Gaussian mixture model representation of endmember variability in hyperspectral unmixing
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• Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
• Illumination invariant hyperspectral image unmixing based on a digital surface model
• Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
• Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
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