|Author||Halimi, Abderrahim ♦ Honeine, Paul ♦ Bioucas-Dias, José ♦ Buller, Gerald S. ♦ Mclaughlin, Steve|
|Source||Hyper Articles en Ligne (HAL)|
|Subject Keyword||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 ♦ info ♦ spi ♦ Computer Science [cs]/Machine Learning [cs.LG] ♦ Computer Science [cs]/Signal and Image Processing ♦ Engineering Sciences [physics]/Signal and Image processing ♦ Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]|
|Abstract||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.|
|Learning Resource Type||Proceeding|
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