Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

IEEE Transactions on Image Processing, no. 12 (2015): 4904-4917

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Abstract

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is ...More

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