IEEE Trans Image Process - Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery.

Tópicos

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Resumo

This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers. These endmembers are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, we model endmembers as Gaussian vectors whose means have been determined using an endmember extraction algorithm such as the famous N-finder (N-FINDR) or Vertex Component Analysis (VCA) algorithms. This paper proposes to estimate the mixture coefficients (referred to as abundances) using a Bayesian algorithm. Suitable priors are assigned to the abundances in order to satisfy positivity and additivity constraints whereas conjugate priors are chosen for the remaining parameters. A hybrid Gibbs sampler is then constructed to generate abundance and variance samples distributed according to the joint posterior of the abundances and noise variances. The performance of the proposed methodology is evaluated by comparison with other unmixing algorithms on synthetic and real images.

Resumo Limpo

paper studi new bayesian unmix algorithm hyperspectr imag pixel imag model linear combin socal endmemb endmemb suppos random order model uncertainti regard knowledg precis model endmemb gaussian vector whose mean determin use endmemb extract algorithm famous nfinder nfindr vertex compon analysi vca algorithm paper propos estim mixtur coeffici refer abund use bayesian algorithm suitabl prior assign abund order satisfi posit addit constraint wherea conjug prior chosen remain paramet hybrid gibb sampler construct generat abund varianc sampl distribut accord joint posterior abund nois varianc perform propos methodolog evalu comparison unmix algorithm synthet real imag

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