Enhancing Binary Change Detection Performance Using A Moving Threshold Window (MTW) Approach

Abstract

This study introduced a new concept, the Moving Threshold Window (MTW), for binary change detection. An automated MTW-based calibration model was developed and evaluated using a case study. The MTW-based model is free from the assumption of symmetry for difference and ratio types of change-enhanced images, unlike traditional binary change detection methods. The MTW-based calibration model outperformed the traditional binary change detection methods based on the Symmetric Threshold Window (STW) for both single and multiple change-enhanced images of the study area. In most of the calibrations, the optimum thresholds resulting in the highest Kappa coefficient were asymmetric. Three major factors may explain the asymmetric characteristics of the optimum thresholds, including: different atmospheric conditions found in the two dates of imagery, different look angles associated with the two dates of imagery, and the nature of the change information. Multiple change-enhanced images generally produced higher accuracies than single change-enhanced images using both the MTW- and STW-based models.