TY - GEN
T1 - Super resolution of multispectral images using locally adaptive models
AU - Molina, Rafael
AU - Mateos, Javier
AU - Vega, Miguel
AU - Katsaggelos, Aggelos K.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:60649116532
SN - 9788392134022
T3 - European Signal Processing Conference
SP - 1497
EP - 1501
BT - 15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings
T2 - 15th European Signal Processing Conference, EUSIPCO 2007
Y2 - 3 September 2007 through 7 September 2007
ER -