An automated binary change detection model using a calibration approach

An automated binary change detection model using a threshold-based calibration approach was introduced in the study. The burdensome processes required in binary change detection, including calibration, calculation of accuracy, extraction of optimum threshold(s), generation of a binary change mask, and removal of “salt-and-pepper” noise were integrated and automated in the model. For practical purpose, the model was implemented as a dynamic linked library in ESRI ArcMap 9.1 using Visual Basic. This study demonstrated the model with a variety of single and multiple variables (layers) extracted from multiple-date QuickBird imagery for three study sites in Las Vegas, NV and two study sites in Tucson, AZ. The use of multiple variables in binary change detection resulted in significantly better performance than single variables.