A New Thinking of Lulc Classification Accuracy Assessment

Abstract

<p><strong>Abstract.</strong> 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.</p>

Quantifying Uncertainty in Land-Use/Land-Cover Classification Accuracy: A Stochastic Simulation Approach

“…The corresponding author thanks the staff members at the Center for Southeast Asian Studies of Kyoto University, Japan, for hosting his sabbatical leave and providing an excellent research environment and facilities. This work is an extension of an article presented at the International Society for Photogrammetry and Remote Sensing Geospatial Week 2019 (Cheng et al, 2019).…”