Nonlinear hyperspectral unmixing accounting for spatial illumination variability

Abderrahim Halimi (1) , Paul Honeine (2) , José Bioucas-Dias (3) , Gerald S. Buller (1) , Steve Mclaughlin (1)

1 HWU - Heriot-Watt University [Edinburgh]
2 DocApp - LITIS - Equipe Apprentissage
3 Instituto de Telecomunicações [Lisboa, Portugal]

This paper presents a new supervised algorithm for nonlinear hyperspectral unmixing. Based on the residual component analysis model, the proposed model assumes the linear model to be corrupted by an additive term that accounts for bilinear interactions between the endmembers. The proposed formulation considers also the effect of the spatial illumination variability. The parameters of the proposed model are estimated using a Bayesian strategy. This approach introduces prior distributions on the parameters of interest to take into account their known constraints. The resulting posterior distribution is optimized using a coordinate descent algorithm which allows us to approximate 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; Bayes methods; computational complexity; image processing; maximum likelihood estimation; coordinate descent algorithm; unknown model parameters; estimation algorithm; state-of-the-art algorithms; spatial illumination variability; supervised algorithm; residual component analysis model; linear model; additive term; bilinear interactions; Bayesian strategy; prior distributions; maximum a posteriori estimator; posterior distribution; nonlinear hyperspectral unmixing; Computational modeling; Hyperspectral imaging; Lighting; Inference algorithms; Estimation; Mixture models; Hyperspectral; nonlinear unmixing; Bayesian estimation; coordinate descent; gamma Markov random field

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Abderrahim Halimi, Paul Honeine, José Bioucas-Dias, Gerald S. Buller, Steve Mclaughlin. Nonlinear hyperspectral unmixing accounting for spatial illumination variability. Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016, Los Angeles, CA, United States. ⟨10.1109/WHISPERS.2016.8071750⟩. ⟨hal-01965918⟩