EGU2020-10187
https://doi.org/10.5194/egusphere-egu2020-10187
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Time-SIFT : a frugal method for leveraging multi-temporal photogrammetric data without ancillary data

Denis Feurer1, Sean Bemis2, Guillaume Coulouma1, Hatem Mabrouk3, Sylvain Massuel4, Romina Vanessa Barbosa5, Yoann Thomas5, Jérôme Ammann6, and Fabrice Vinatier1
Denis Feurer et al.
  • 1LISAH, Univ Montpellier, INRAE, IRD, Montpellier SupAgro, Montpellier, France (denis.feurer@ird.fr)
  • 2Virginia Tech University, 250 S Main St., Blacksburg, VA, USA
  • 3INAT, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia
  • 4G-EAU, AgroParisTech, Cirad, IRD, IRSTEA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
  • 5UMR 6539 LEMAR, CNRS, UBO, IRD, Ifremer, IUEM Plouzané, France
  • 6Université de Bretagne Occidentale, CNRS UMR 6538 LGO, IUEM Plouzané, France

Latest advances in lightweight aerial platforms, miniaturized RTK DGPS positioning and IMUs make now it possible to build multitemporal photogrammetric datasets with centrimetric accuracies. Together with the increase of very high-resolution topographic data availability, algorithms that came from the computer vision community also provoked a marked resurgence of interest on archival photogrammetric data. Recently, Feurer and Vinatier (2018) proposed a method that rely on the invariance properties of the feature detection algorithms such as SIFT to estimate orientations in a single multi-temporal block. This method allows for an inherent co-registration of processed multi-temporal photogrammetric datasets and hence detection and mapping of 3-D change from past imagery. This work demonstrated that – in the case of archival aerial imagery – the Time-SIFT method enables the processing of multi-temporal photogrammetric imagery without ancillary data.

However, the potential of the Time-SIFT method had to be checked for in various contexts and spatio-temporal scales. More, the Time-SIFT method may allow to cope with the lack of precise positioning, in the case of image acquisitions made with frugal acquisition systems for instance. Hence this study proposes to apply the Time-SIFT method on five contrasting test cases. Their time and space scales vary from a domain of several square-centimeters to domains of several tens of kilometers, with time spans varying from the minutes to the decades. The test cases rely within different disciplines of geosciences, from soil science to vulcanology. Our works showed that the Time-SIFT methods succeeds through this whole range of spatio-temporal scales, and show even some unexpected robustness in context of strong changes due to vegetation and/or presence of water in coastal areas. These results demonstrate that the Time-SIFT method has a potential to tackle a wide variety of multi-temporal photogrammetric datasets, in particular in contexts where additional and calibration data are scarce.

How to cite: Feurer, D., Bemis, S., Coulouma, G., Mabrouk, H., Massuel, S., Barbosa, R. V., Thomas, Y., Ammann, J., and Vinatier, F.: Time-SIFT : a frugal method for leveraging multi-temporal photogrammetric data without ancillary data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10187, https://doi.org/10.5194/egusphere-egu2020-10187, 2020

Display materials

Display file

Comments on the display material

AC: Author Comment | CC: Community Comment | Report abuse

Display material version 1 – uploaded on 07 May 2020
  • CC1: Comment on EGU2020-10187, Vincent Regard, 08 May 2020

    Hi

    I am interested in rocky shore evolution. Processes there can be very slow. Can you indicate what kind of precision can you reach with 50-70yrs-old aerial photos?

    • AC1: Reply to CC1, Denis Feurer, 08 May 2020

      Dear Vincent,

      For the rocky shore test case, the interest was evolution of mussels population within the season. Images we used were only on a time span of less than a year. They were UAV images. We did not yet tested rocky shore evolution on larger time spans, but it would indeed be very intreresting to do so.

      Sorry not to be able to better answer your question ; I'll be happy to keep commenting about this subject.

      Denis

  • CC2: Comment on EGU2020-10187, Mike James, 10 May 2020

    Thanks for your contribution - great to see this method works on a wide range of scenarios. It has been useful to refresh my memory of this approach. Looking forward to seeing more of it in the future.

    • AC2: Reply to CC2, Denis Feurer, 10 May 2020

      Many thanks to you for your interest and your questions. By the way I apologize I could not be there for the chat session in the morning.

      The full results of the study presented here were submitted as a book chapter ; I hope the full details will hence soon be available to the community.

      I also plan to develop an Agisoft Photoscan plugin so that other people may more easily test the Time-SIFT method on their own datasets. Don't hesitate to do so and to give me more feedback, I would be very interested in prolonging the discussions on other test cases especially with the expertise of other people used to track error sources in photogrammetric processing.
      By the way, I'm pretty conviced that, by using tie points that go through different epochs and independent blocks of cameras, the Time-SIFT method allows to mitigate the doming effect observed in the case of poor lens autocalibration due to ill-conditioned image acquisition plans and unwanted coupling between focal length and acquisition distance.