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

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>2009
<mark>Journal</mark>International Journal of Remote Sensing
Issue number20
Volume30
Number of pages16
Pages (from-to)5293-5308
Publication StatusPublished
Early online date29/09/09
<mark>Original language</mark>English

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.

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