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The Computer Journal Advance Access originally published online on June 11, 2008
The Computer Journal 2009 52(1):153-167; doi:10.1093/comjnl/bxn031
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© The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Super-Resolution of Multispectral Images

M. Vega1,*, J. Mateos2, R. Molina2 and A.K. Katsaggelos3

1 Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, 18071 Granada, Spain
2 Departamento de Ciencias de la Computación e I. A., Universidad de Granada, 18071 Granada, Spain
3 Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208-3118, USA

* Corresponding author: mvega{at}ugr.es

Received 16 October 2007; revised 6 March 2008

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.

Key Words: super-resolution • Bayesian models • hyperspectral images


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