  This paper addresses the task of time separated aerial image registration.
The ability to solve this problem accurately and reliably is important for a
variety of subsequent image understanding applications. The principal challenge
lies in the extent and nature of transient appearance variation that a land
area can undergo, such as that caused by the change in illumination conditions,
seasonal variations, or the occlusion by non-persistent objects (people, cars).
Our work introduces several novelties: (i) unlike all previous work on aerial
image registration, we approach the problem using a set-based paradigm; (ii) we
show how local, pair-wise constraints can be used to enforce a globally good
registration using a constraints graph structure; (iii) we show how a simple
holistic representation derived from raw aerial images can be used as a basic
building block of the constraints graph in a manner which achieves both high
registration accuracy and speed. We demonstrate: (i) that the proposed method
outperforms the state-of-the-art for pair-wise registration already, achieving
greater accuracy and reliability, while at the same time reducing the
computational cost of the task; and (ii) that the increase in the number of
available images in a set consistently reduces the average registration error.
