Efficient and accurate set-based registration of time-separated aerial images

Oggie Arandelovic*, Duc Son Pham, Svetha Venkatesh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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 under illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several major novelties (i) unlike previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how image space 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; (iv) lastly, we introduce a new and, to the best of our knowledge, the only data corpus suitable for the evaluation of set-based aerial image registration algorithms. Using this data set, 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, with a major difference already for a single additional image.

Original languageEnglish
Pages (from-to)3466-3476
Number of pages11
JournalPattern Recognition
Issue number11
Publication statusPublished - 1 Nov 2015


  • Alignment
  • Constraints
  • Graph
  • Map
  • Remote
  • Sensing


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