Hierarchical Bayesian super resolution reconstruction of multispectral images

Rafael Molina*, Miguel Vega, Javier Mateos, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper we present a super resolution Bayesian methodology for pansharpening of multispectral images which: a) incorporates prior knowledge on the expected characteristics of the multispectral images, b) uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, c) includes information on the unknown parameters in the model, and d) allows for the estimation of both the parameters and the high resolution multispectral image. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality assessed both qualitatively and quantitatively.

Original languageEnglish (US)
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2006

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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