Accurate geo-location of imagery produced from airborne imaging sensors is a prerequisite for precision targeting and
navigation. However, the geo-location metadata often has significant errors which can degrade the performance of
applications using the imagery. When reference imagery is available, image registration can be performed as part of a
bundle-adjustment procedure to reduce metadata errors. Knowledge of the metadata error statistics can be used to set the
registration transform hypothesis search space size. In setting the search space size, a compromise is often made between
computational expediency and search space coverage. It therefore becomes necessary to detect cases in which the true
registration solution falls outside of the initial search space. To this end, we develop a registration verification metric, for
use in a multisensor image registration algorithm, which measures the verity of the registration solution. The verification
metric value is used in a hypothesis testing problem to make a decision regarding the suitability of the search space size.
Based on the hypothesis test outcome, we close the loop on the verification metric in an iterative algorithm. We expand
the search space as necessary, and re-execute the registration algorithm using the expanded search space. We first
provide an overview of the registration algorithm, and then describe the verification metric. We generate numerical
results of the verification metric hypothesis testing problem in the form of Receiver Operating Characteristics curves
illustrating the accuracy of the approach. We also discuss normalization of the metric across scene content.