Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study
Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study
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DOI：
10.1080/01431160903131034
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发表时间：
2009-01-01
影响因子：
3.4
通讯作者：
Atkinson, Peter M.
中科院分区：
文献类型：
--
作者：
Atkinson, Peter M.
超分辨率制图在遥感领域是一个相对较新的领域，因此分类是以比输入的遥感多波段图像更精细的空间分辨率进行的。人们已经提出了各种不同的超分辨率映射方法，包括空间像元交换、空间模拟退火法、Hopfield神经网络、前馈反向传播神经网络和地质统计学方法。所有这些新方法的准确性都经过了测试，但测试往往侧重于新技术(即很少与其他技术进行基准比较)，并使用了不同的准确性衡量标准。因此，有必要加强现有各种方法之间的相互比较，而超分辨率的相互比较研究将是朝着这一目标迈出的可喜的一步。本文描述了在设计此类研究时应考虑的一些问题。
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.