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Multi-temporal Registration of Environmental Imagery Using Affine Invariant Convolutional Features

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Image and Video Technology (PSIVT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11854))

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Abstract

Repeat photography is a practice of collecting multiple images of the same subject at the same location but at different timestamps for comparative analysis. The visualisation of such imagery can provide a valuable insight for continuous monitoring and change detection. In Victoria, Australia, citizen science and environmental monitoring are integrated through the visitor-based repeat photography of national parks and coastal areas. Repeat photography, however, poses enormous challenges for automated data analysis and visualisation due to variations in viewpoints, scales, luminosity and camera attributes. To address these challenges brought by data variability, this paper introduces a robust multi-temporal image registration approach based on affine invariance and convolutional neural network architecture. Our experimental evaluation on a large repeat photography dataset validates the role of multi-temporal image registration for better visualisation of environmental monitoring imagery. Our research will establish a baseline for the broad area of multi-temporal analysis.

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Author information

Authors and Affiliations

  1. Institute for Sustainable Industries Liveable Cities, Victoria University, Melbourne, VIC, Australia

    Asim Khan, Anwaar Ulhaq & Randall W. Robinson

  2. Charles Sturt University, Dubbo, NSW, Australia

    Anwaar Ulhaq

Authors
  1. Asim Khan
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  2. Anwaar Ulhaq
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  3. Randall W. Robinson
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Corresponding authors

Correspondence to Asim Khan or Anwaar Ulhaq .

Editor information

Editors and Affiliations

  1. Chonnam National University, Gwangju, Korea (Republic of)

    Chilwoo Lee

  2. Dalian University of Technology, Dalian, China

    Zhixun Su

  3. National Institute of Informatics, Tokyo, Japan

    Akihiro Sugimoto

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Khan, A., Ulhaq, A., Robinson, R.W. (2019). Multi-temporal Registration of Environmental Imagery Using Affine Invariant Convolutional Features. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-34879-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34878-6

  • Online ISBN: 978-3-030-34879-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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