@article{Aggelos2007e,
abstract = {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.},
author = {Vega, M. and Mateos, J. and Molina, R. and Katsaggelos, A.K.},
doi = {10.1093/comjnl/bxn031},
issn = {0010-4620},
journal = {The Computer Journal},
keywords = {Bayesian models,Hyperspectral images,Super-resolution},
month = {feb},
number = {1},
pages = {153--167},
title = {{Super-Resolution of Multispectral Images}},
url = {https://academic.oup.com/comjnl/article-lookup/doi/10.1093/comjnl/bxn031},
volume = {52},
year = {2008}
}

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