Robust hyperspectral unmixing accounting for residual components

Abderrahim Halimi, Paul Honeine, Jose M. Bioucas-Dias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
54 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publication2016 IEEE Statistical Signal Processing Workshop (SSP)
PublisherIEEE
ISBN (Electronic)9781467378031
DOIs
Publication statusPublished - 25 Aug 2016
Event19th IEEE Statistical Signal Processing Workshop - Palma de Mallorca, Spain
Duration: 26 Jun 201629 Jun 2016

Conference

Conference19th IEEE Statistical Signal Processing Workshop
Abbreviated titleSSP 2016
CountrySpain
CityPalma de Mallorca
Period26/06/1629/06/16

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  • Cite this

    Halimi, A., Honeine, P., & Bioucas-Dias, J. M. (2016). Robust hyperspectral unmixing accounting for residual components. In 2016 IEEE Statistical Signal Processing Workshop (SSP) [7551848] IEEE. https://doi.org/10.1109/SSP.2016.7551848