https://scholars.lib.ntu.edu.tw/handle/123456789/449005
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. KE-SHENG CHENG |
Issue Date: | 2019 | Journal Volume: | 42 | Journal Issue: | 2/W13 | Start page/Pages: | 1207-1211 | Source: | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | 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. © Authors 2019. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/449005 | ISSN: | 1682-1750 | DOI: | 10.5194/isprs-archives-XLII-2-W13-1207-2019 | SDG/Keyword: | Image resolution; Land use; Remote sensing; Sampling; Stochastic models; Stochastic systems; Accuracy assessment; Bootstrap resampling; Classification accuracy; Classification accuracy assessments; Classification results; Confidence interval; Confusion matrices; Stochastic simulations; Classification (of information) |
Appears in Collections: | 生物環境系統工程學系 |
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