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Hyperspectral unmixing accounting for spatial correlations and endmember variability
2015
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed model is unsupervised since it estimates the abundances and both the mean and the covariance matrix of each
doi:10.1109/whispers.2015.8075442
dblp:conf/whispers/HalimiDTH15
fatcat:6s6sxw6o4fdjnhjxma3hfvhwx4