Parameter estimation in Bayesian reconstruction of multispectral images using super resolution techniques

Rafael Molina; Miguel Vega; Javier Mateos; Aggelos K Katsaggelos

doi:10.1109/ICIP.2006.312720

Parameter estimation in Bayesian reconstruction of multispectral images using super resolution techniques

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

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper we present a new super resolution Bayesian method 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, and c) performs the estimation of all the unknown parameters in the model. Using real 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 language	English (US)
Title of host publication	2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages	1749-1752
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2006.312720
State	Published - Dec 1 2006
Event	2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States Duration: Oct 8 2006 → Oct 11 2006

Other

Other	2006 IEEE International Conference on Image Processing, ICIP 2006
Country	United States
City	Atlanta, GA
Period	10/8/06 → 10/11/06

Keywords

Hierarchical Bayesian modeling
Image reconstruction
Pansharpening multispectral images
Super resolution

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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title = "Parameter estimation in Bayesian reconstruction of multispectral images using super resolution techniques",

abstract = "In this paper we present a new super resolution Bayesian method 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, and c) performs the estimation of all the unknown parameters in the model. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively.",

keywords = "Hierarchical Bayesian modeling, Image reconstruction, Pansharpening multispectral images, Super resolution",

author = "Rafael Molina and Miguel Vega and Javier Mateos and Katsaggelos, {Aggelos K}",

year = "2006",

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doi = "10.1109/ICIP.2006.312720",

language = "English (US)",

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booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",

note = "2006 IEEE International Conference on Image Processing, ICIP 2006 ; Conference date: 08-10-2006 Through 11-10-2006",

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Molina, R, Vega, M, Mateos, J & Katsaggelos, AK 2006, Parameter estimation in Bayesian reconstruction of multispectral images using super resolution techniques. in 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings., 4106888, pp. 1749-1752, 2006 IEEE International Conference on Image Processing, ICIP 2006, Atlanta, GA, United States, 10/8/06. https://doi.org/10.1109/ICIP.2006.312720

Parameter estimation in Bayesian reconstruction of multispectral images using super resolution techniques. / Molina, Rafael; Vega, Miguel; Mateos, Javier; Katsaggelos, Aggelos K.

2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings. 2006. p. 1749-1752 4106888.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Vega, Miguel

AU - Mateos, Javier

AU - Katsaggelos, Aggelos K

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KW - Image reconstruction

KW - Pansharpening multispectral images

KW - Super resolution

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