Groupwise registration of aerial images

TL;DR

This paper introduces a novel set-based aerial image registration method that leverages local pair-wise constraints and holistic image representations to improve accuracy, reliability, and computational efficiency over existing techniques.

Contribution

The work presents a unique set-based registration approach using a constraints graph and holistic image features, outperforming prior pair-wise methods in accuracy and speed.

Findings

Outperforms state-of-the-art pair-wise registration methods

Achieves higher accuracy and reliability

Reduces computational cost

Abstract

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.