Super resolution of multispectral images using ℓ1 image models and interband correlations

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

doi:10.1109/MLSP.2009.5306217

Super resolution of multispectral images using ℓ1 image models and interband correlations

Miguel Vega^*, Javier Mateos, Rafael Molina, 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 propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, imposes smoothness within each band by means of the energy associated to the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation between the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and its quality is assessed both qualitatively and quantitatively.

Original language	English (US)
Title of host publication	Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs	https://doi.org/10.1109/MLSP.2009.5306217
State	Published - Dec 1 2009
Event	Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France Duration: Sep 2 2009 → Sep 4 2009

Publication series

Name	Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Other

Other	Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
Country/Territory	France
City	Grenoble
Period	9/2/09 → 9/4/09

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing
Education

Access to Document

10.1109/MLSP.2009.5306217

Cite this

Vega, M., Mateos, J., Molina, R., & Katsaggelos, A. K. (2009). Super resolution of multispectral images using ℓ1 image models and interband correlations. In Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009 [5306217] (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009). https://doi.org/10.1109/MLSP.2009.5306217

Vega, Miguel ; Mateos, Javier ; Molina, Rafael ; Katsaggelos, Aggelos K. / Super resolution of multispectral images using ℓ1 image models and interband correlations. Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009).

@inproceedings{509dfd4aa9b34ae68aa0fe755ddff118,

title = "Super resolution of multispectral images using ℓ1 image models and interband correlations",

abstract = "In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, imposes smoothness within each band by means of the energy associated to the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation between the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and its quality is assessed both qualitatively and quantitatively.",

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

year = "2009",

month = dec,

day = "1",

doi = "10.1109/MLSP.2009.5306217",

language = "English (US)",

isbn = "9781424449484",

series = "Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009",

booktitle = "Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009",

note = "Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 ; Conference date: 02-09-2009 Through 04-09-2009",

}

Vega, M, Mateos, J, Molina, R & Katsaggelos, AK 2009, Super resolution of multispectral images using ℓ1 image models and interband correlations. in Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009., 5306217, Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009, Grenoble, France, 9/2/09. https://doi.org/10.1109/MLSP.2009.5306217

Super resolution of multispectral images using ℓ1 image models and interband correlations. / Vega, Miguel; Mateos, Javier; Molina, Rafael; Katsaggelos, Aggelos K.

Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. 5306217 (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009).

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

TY - GEN

T1 - Super resolution of multispectral images using ℓ1 image models and interband correlations

AU - Vega, Miguel

AU - Mateos, Javier

AU - Molina, Rafael

AU - Katsaggelos, Aggelos K

PY - 2009/12/1

Y1 - 2009/12/1

N2 - In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, imposes smoothness within each band by means of the energy associated to the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation between the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and its quality is assessed both qualitatively and quantitatively.

AB - In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, imposes smoothness within each band by means of the energy associated to the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation between the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and its quality is assessed both qualitatively and quantitatively.

UR - http://www.scopus.com/inward/record.url?scp=77950960267&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77950960267&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2009.5306217

DO - 10.1109/MLSP.2009.5306217

M3 - Conference contribution

AN - SCOPUS:77950960267

SN - 9781424449484

T3 - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

BT - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

T2 - Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Y2 - 2 September 2009 through 4 September 2009

ER -

Vega M, Mateos J, Molina R, Katsaggelos AK. Super resolution of multispectral images using ℓ1 image models and interband correlations. In Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. 5306217. (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009). https://doi.org/10.1109/MLSP.2009.5306217

Super resolution of multispectral images using ℓ1 image models and interband correlations

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this