Assessment of automated multitemporal image co-registration using repeat station imaging techniques

Repeat station imaging (RSI) is a method for specialized image collection and co-registration that facilitates rapid change detection with aerial imagery for time-critical analyses. Our previously reported research has defined methods for automated multitemporal image co-registration and demonstrated the utility of RSI for achieving precise co-registration, but without actually automating the technique. For this paper, we developed a custom software implementing specific procedures for automated RSI-based image co-registration, processed 252 image pairs containing diverse scenes and collection conditions, and evaluated the performance of RSI and the auto-registration tool. We found that the average root-mean-square error of image co-registration ranged between 1.3 and 1.9 pixels for aerial RSI images with 8-14 cm spatial resolution captured at the same time of day. The implications of these findings are that automated multitemporal co-registration and automated change detection could be performed in near real-time onboard an aircraft as it flies, opening up a range of new monitoring applications, particularly with unmanned aircraft systems. However, results with our custom software also indicate that images captured at different times of day with varying illumination and shadow conditions yield poor co-registration, and in some instances fail to register.