Issues of Uncertainty in Super-Resolution Mapping and the Design of an Inter-Comparison Study

Peter M. Atkinson
School of Geography, University of Southampton, Highfield, Southampton, SO17 1BJ, UK

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 pixelswapping, spatial simulated annealing and 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 been individual (i.e., with little bench-marking 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.

Keywords: super-resolution mapping, inter-comparison, accuracy assessment

In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).

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