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Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10746))

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Abstract

This paper proposes using both spatial and spectral regularizers/priors for hyperspectral image sharpening. Leveraging the recent plug-and-play framework, we plug two Gaussian-mixture-based denoisers into the iterations of an alternating direction method of multipliers (ADMM): a spatial regularizer learned from the observed multispectral image, and a spectral regularizer trained using the hyperspectral data. The proposed approach achieves very competitive results, improving the performance over using a single regularizer. Furthermore, the spectral regularizer can be used to classify the image pixels, opening the door to class-adapted models.

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Notes

  1. 1.

    https://speclab.cr.usgs.gov/spectral-lib.html.

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Acknowledgments

This work was partially supported by Fundação para a Ciência e Tecnologia (FCT), grants BD/102715/2014, UID/EEA/5008/2013, and ERANETMED/0001/2014. The authors would like to thank Prof. N. Yokoya for providing the datasets [26].

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Authors and Affiliations

  1. Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Afonso M. Teodoro, José M. Bioucas-Dias & Mário A. T. Figueiredo

Authors
  1. Afonso M. Teodoro
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  2. José M. Bioucas-Dias
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  3. Mário A. T. Figueiredo
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Corresponding author

Correspondence to Mário A. T. Figueiredo .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. University of York, York, United Kingdom

    Edwin Hancock

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Teodoro, A.M., Bioucas-Dias, J.M., Figueiredo, M.A.T. (2018). Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm. In: Pelillo, M., Hancock, E. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science(), vol 10746. Springer, Cham. https://doi.org/10.1007/978-3-319-78199-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-78199-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78198-3

  • Online ISBN: 978-3-319-78199-0

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