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- Title
A NEW THINKING OF LULC CLASSIFICATION ACCURACY ASSESSMENT.
- Authors
Cheng, K. S.; Ling, J. Y.; Lin, T. W.; Liu, Y. T.; Shen, Y. C.; Kono, Y.
- Abstract
A majority of studies involving remote sensing LULC classification conducted classification accuracy assessment without consideration of the training data uncertainty. In this study we present new concepts of LULC classification accuracies, namely the training-sample-based global accuracy and the classifier global accuracy, and a general expression of different measures of classification accuracy in terms of the sample dataset for classifier training and the sample dataset for evaluation of classification results. Through stochastic simulation of a two-feature and two-class case, we demonstrate that the training-sample confusion matrix should replace the commonly adopted reference-sample confusion matrix for evaluation of LULC classification results. We then propose a bootstrap-simulation approach for establishing 95% confidence intervals of classifier global accuracies.
- Subjects
CLASSIFICATION; REMOTE sensing; CONFIDENCE intervals
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2019, Vol XLII-2/W13, p1207
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLII-2-W13-1207-2019