MERCURY ⊕ : an evidential reasoning image classifier
Computers & Geosciences
The kappa statistic: a second look
Computational Linguistics
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
RVM-based multi-class classification of remotely sensed data
International Journal of Remote Sensing
International Journal of Remote Sensing
An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation
International Journal of Geographical Information Science
Increasing the accuracy of neural network classification using refined training data
Environmental Modelling & Software
The impact of imperfect ground reference data on the accuracy of land cover change estimation
International Journal of Remote Sensing
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Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing.