Super resolution of multispectral images using locally adaptive models

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we present a locally adaptive super resolution Bayesian methodology for pansharpening of multispectral images. The proposed method incorporates prior local 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 parsharpening methods and their quality is assessed both qualitatively and quantitatively.

Original languageEnglish (US)
Title of host publication15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings
Pages1497-1501
Number of pages5
StatePublished - Dec 1 2007
Event15th European Signal Processing Conference, EUSIPCO 2007 - Poznan, Poland
Duration: Sep 3 2007Sep 7 2007

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other15th European Signal Processing Conference, EUSIPCO 2007
CountryPoland
CityPoznan
Period9/3/079/7/07

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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    Molina, R., Mateos, J., Vega, M., & Katsaggelos, A. K. (2007). Super resolution of multispectral images using locally adaptive models. In 15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings (pp. 1497-1501). (European Signal Processing Conference).