Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hyperspectral Unmixing with Endmember Variability using Partial
  Membership Latent Dirichlet Allocation   The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for
hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA
provides a model for a hyperspectral image analysis that accounts for spectral
variability and incorporates spatial information through the use of
superpixel-based 'documents.' In our application of PM-LDA, we employ the
Normal Compositional Model in which endmembers are represented as Normal
distributions to account for spectral variability and proportion vectors are
modeled as random variables governed by a Dirichlet distribution. The use of
the Dirichlet distribution enforces positivity and sum-to-one constraints on
the proportion values. Algorithm results on real hyperspectral data indicate
that PM-LDA produces endmember distributions that represent the ground truth
classes and their associated variability.

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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat