Accuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison

Accuracy assessment for remote sensing classification is commonly based on using an error matrix, or confusion table, which needs reference, or ‘ground truthing’, data to support. When undertaking change detection using numerous multi-temporal images, it is often difficult to make the accuracy assessment by the ‘traditional’ method, which typically requires simultaneous collection of reference data. In this study, we propose a new approach by arguing change rationality with post-classification comparison. Multi-temporal Landsat TM images were classified for land use in an urban fringe area of Beijing, China and the post-classification comparison of these classified images shows change trajectories through the time series. These change trajectories were then analysed by assessing their rationality against a set of logical rules to separate cases of ‘real land use change’ and possible classification errors. The analysis results show that the overall accuracy for land use change in the urban fringe area was 86%, with a fuzziness of 7%. Although it is argued that the uncertainty still exists on classification accuracy assessed by this method, it nevertheless provides an alternative approach for more reasonable assessment when ideal simultaneous ‘ground truthing’ is not available.