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Downscaling cokriging for super-resolution mapping of continua in remotely sensed images

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Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. / Atkinson, Peter M.; Pardo-Iguzquiza, E.; Chico-Olmo, M.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 2, 02.2008, p. 573-580.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Atkinson, PM, Pardo-Iguzquiza, E & Chico-Olmo, M 2008, 'Downscaling cokriging for super-resolution mapping of continua in remotely sensed images', IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 2, pp. 573-580. https://doi.org/10.1109/TGRS.2007.909952

APA

Atkinson, P. M., Pardo-Iguzquiza, E., & Chico-Olmo, M. (2008). Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 573-580. https://doi.org/10.1109/TGRS.2007.909952

Vancouver

Atkinson PM, Pardo-Iguzquiza E, Chico-Olmo M. Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing. 2008 Feb;46(2):573-580. https://doi.org/10.1109/TGRS.2007.909952

Author

Atkinson, Peter M. ; Pardo-Iguzquiza, E. ; Chico-Olmo, M. / Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. In: IEEE Transactions on Geoscience and Remote Sensing. 2008 ; Vol. 46, No. 2. pp. 573-580.

Bibtex

@article{e934f4bee5944ec8a41c478ca88eab72,
title = "Downscaling cokriging for super-resolution mapping of continua in remotely sensed images",
abstract = "The main aim of this paper is to show the implementation and application of downscaling cokriging for super-resolution image mapping. By super-resolution, we mean increasing the spatial resolution of satellite sensor images where the pixel size to be predicted is smaller than the pixel size of the empirical image with the finest spatial resolution. It is assumed that coregistered images with different spatial and spectral resolutions of the same scene are available. The main advantages of cokriging are that it takes into account the correlation and cross correlation of images, it accounts for the different supports (i.e., pixel sizes), it can explicitly take into account the point spread function of the sensor, and it has the property of prediction coherence. In addition, ancillary images (topographic maps, thematic maps, etc.) as well as sparse experimental data could be included in the process. The main problem is that super-resolution cokriging requires several covariances and cross covariances, some of which are not empirically accessible (i.e., from the pixel values of the images). In the adopted solution, the fundamental concept is that of covariances and cross-covariance models with point support. Once the set of point-support models is estimated using linear systems theory, any pixel-support covariance and cross covariance can be easily obtained by regularization. We show the performance of the method using Landsat Enhanced Thematic Mapper Plus images.",
author = "Atkinson, {Peter M.} and E. Pardo-Iguzquiza and M. Chico-Olmo",
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language = "English",
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journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
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RIS

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T1 - Downscaling cokriging for super-resolution mapping of continua in remotely sensed images

AU - Atkinson, Peter M.

AU - Pardo-Iguzquiza, E.

AU - Chico-Olmo, M.

N1 - M1 - 2

PY - 2008/2

Y1 - 2008/2

N2 - The main aim of this paper is to show the implementation and application of downscaling cokriging for super-resolution image mapping. By super-resolution, we mean increasing the spatial resolution of satellite sensor images where the pixel size to be predicted is smaller than the pixel size of the empirical image with the finest spatial resolution. It is assumed that coregistered images with different spatial and spectral resolutions of the same scene are available. The main advantages of cokriging are that it takes into account the correlation and cross correlation of images, it accounts for the different supports (i.e., pixel sizes), it can explicitly take into account the point spread function of the sensor, and it has the property of prediction coherence. In addition, ancillary images (topographic maps, thematic maps, etc.) as well as sparse experimental data could be included in the process. The main problem is that super-resolution cokriging requires several covariances and cross covariances, some of which are not empirically accessible (i.e., from the pixel values of the images). In the adopted solution, the fundamental concept is that of covariances and cross-covariance models with point support. Once the set of point-support models is estimated using linear systems theory, any pixel-support covariance and cross covariance can be easily obtained by regularization. We show the performance of the method using Landsat Enhanced Thematic Mapper Plus images.

AB - The main aim of this paper is to show the implementation and application of downscaling cokriging for super-resolution image mapping. By super-resolution, we mean increasing the spatial resolution of satellite sensor images where the pixel size to be predicted is smaller than the pixel size of the empirical image with the finest spatial resolution. It is assumed that coregistered images with different spatial and spectral resolutions of the same scene are available. The main advantages of cokriging are that it takes into account the correlation and cross correlation of images, it accounts for the different supports (i.e., pixel sizes), it can explicitly take into account the point spread function of the sensor, and it has the property of prediction coherence. In addition, ancillary images (topographic maps, thematic maps, etc.) as well as sparse experimental data could be included in the process. The main problem is that super-resolution cokriging requires several covariances and cross covariances, some of which are not empirically accessible (i.e., from the pixel values of the images). In the adopted solution, the fundamental concept is that of covariances and cross-covariance models with point support. Once the set of point-support models is estimated using linear systems theory, any pixel-support covariance and cross covariance can be easily obtained by regularization. We show the performance of the method using Landsat Enhanced Thematic Mapper Plus images.

U2 - 10.1109/TGRS.2007.909952

DO - 10.1109/TGRS.2007.909952

M3 - Journal article

VL - 46

SP - 573

EP - 580

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 2

ER -