Preprints
https://doi.org/10.5194/hess-2021-112
https://doi.org/10.5194/hess-2021-112

  12 Apr 2021

12 Apr 2021

Review status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

A comparison of tools and techniques for stabilising UAS imagery for surface flow observations

Robert Ljubičić1, Dariia Strelnikova2, Matthew T. Perks3, Anette Eltner4, Salvador Peña-Haro5, Alonso Pizarro6, Silvano Fortunato Dal Sasso7, Ulf Scherling2, Pietro Vuono7, and Salvatore Manfreda6 Robert Ljubičić et al.
  • 1Department of Hydraulic and Environmental Engineering, Faculty of Civil Engineering, University of Belgrade, 11120 Serbia
  • 2Carinthia University of Applied Sciences, School of Geoinformation, Villach 9524, Austria
  • 3School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
  • 4Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01069 Dresden, Germany
  • 5Photrack AG, Ankerstrasse 16a, 8004 Zurich, Switzerland
  • 6Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
  • 7Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DICEM), University of Basilicata, 75100 Matera, Italy

Abstract. While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements are still to be quantified and mitigated. Physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements such as velocimetry. A common practice in the data preprocessing stages is the compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of physically static features – to first estimate and then to compensate such motion. Most existing stabilisation approaches rely either on in-house built tools based on different algorithms, or on general-purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for a selection of a particular tool in given conditions has not been found in the available literature. In this paper we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry purposes. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root-mean-squared differences (RMSD) of interframe pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying and selecting static features in videos – manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed a light on implementational details which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can also be used in order to measure the velocity estimation uncertainty induced by UAS motion.

Robert Ljubičić et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-112', Anonymous Referee #1, 05 May 2021
    • AC1: 'Reply on RC1', Robert Ljubicic, 03 Jun 2021
  • RC2: 'Comment on hess-2021-112', Anonymous Referee #2, 14 May 2021
    • AC2: 'Reply on RC2', Robert Ljubicic, 03 Jun 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-112', Anonymous Referee #1, 05 May 2021
    • AC1: 'Reply on RC1', Robert Ljubicic, 03 Jun 2021
  • RC2: 'Comment on hess-2021-112', Anonymous Referee #2, 14 May 2021
    • AC2: 'Reply on RC2', Robert Ljubicic, 03 Jun 2021

Robert Ljubičić et al.

Robert Ljubičić et al.

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Latest update: 21 Sep 2021
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Short summary
The rise of new technologies such as drones (unmanned aerial systems, UASs) has allowed the widespread use of image velocimetry techniques in place of more traditional, usually slower methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, a performance comparison of different UAS video stabilisation tools, and provides some guidelines for their use in videos with different flight and ground conditions.