A comparison of tools and techniques for stabilising UAS imagery for surface flow observations
Authors: Ljubičić Robert , Strelnikova Dariia , Perks Matthew T. , Eltner Anette , Peña-Haro Salvador , Pizarro Alonso , Dal Sasso Silvano Fortunato , Scherling Ulf , Vuono Pietro , Manfreda Salvatore 
Published: April 2021

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.