Super-resolution of multispectral images

M. Vega*, J. Mateos, R. Molina, A. K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


In this paper we propose and analyze a globally and locally adaptive super-resolution Bayesian methodology for pansharpening of multispectral images. The methodology incorporates prior knowledge on the expected characteristics of the multispectral images uses the sensor characteristics to model the observation process of both panchromatic and multispectral images and includes information on the unknown parameters in the model in the form of hyperprior distributions. Using real and synthetic data, the pansharpened multispectral images are compared with the images obtained by other pansharpening methods and their quality is assessed both qualitatively and quantitatively. The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Original languageEnglish (US)
Pages (from-to)153-167
Number of pages15
JournalComputer Journal
Issue number1
StatePublished - Jan 1 2009


  • Bayesian models
  • Hyperspectral images
  • Super-resolution

ASJC Scopus subject areas

  • Computer Science(all)


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