Sensitivity of anomalous change detection to small misregistration errors

Arguably the single greatest confound for change detection algorithms is the misregistration of the two images in which changes are being sought. On the other hand, since the effects of misregistration are exhibited over the entire image, there is reason to hope that algorithms which are designed to deal with pervasive effects (such as illumination differences in the scene, or calibration drifts in the sensor) will be less sensitive to the inevitable misregistration errors that occur when comparing two images. This work will describe some controlled experiments in which change detection performance is evaluated as a function of how misregistered the images are. The performance is observed to degrade quite rapidly with the amount of misregistration (so that any practical system for automated change detection will require accurate image registration), but algorithms that are more adaptive to pervasive differences are less sensitive to the effect of misregistration.