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Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study

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Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study. / Atkinson, Peter M.

In: International Journal of Remote Sensing, Vol. 30, No. 20, 2009, p. 5293-5308.

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@article{41fead2bc38849b0b72d05d01420adfc,
title = "Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study",
abstract = "Super-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study.",
author = "Atkinson, {Peter M.}",
note = "M1 - 20",
year = "2009",
doi = "10.1080/01431160903131034",
language = "English",
volume = "30",
pages = "5293--5308",
journal = "International Journal of Remote Sensing",
issn = "0143-1161",
publisher = "TAYLOR & FRANCIS LTD",
number = "20",

}

RIS

TY - JOUR

T1 - Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study

AU - Atkinson, Peter M.

N1 - M1 - 20

PY - 2009

Y1 - 2009

N2 - Super-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study.

AB - Super-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study.

U2 - 10.1080/01431160903131034

DO - 10.1080/01431160903131034

M3 - Journal article

VL - 30

SP - 5293

EP - 5308

JO - International Journal of Remote Sensing

JF - International Journal of Remote Sensing

SN - 0143-1161

IS - 20

ER -