Analysis of Imagery – Image Sequences Processing

Measuring object displacement and deformation in image sequences is an important task in remote sensing, photogrammetry and computer vision and a vast number of approaches have been introduced. In the field of environmental sciences, applications are, for instance, in the studies of landslides, tectonic displacements, glaciers, and river flows (Manfreda et al., 2018). Tracking algorithms are vastly utilized for monitoring purposes in terrestrial settings and in satellite remote sensing, which need to be adapted for the application with UAV imagery because resolution, frequency and perspective are different. For instance, geometric and radiometric distortion need to be minimal for successful feature tracking, which can be a large issue for UAV imagery in contrast to satellite imagery with much smaller image scales (Gruen, 2012).

Using UAV systems for multi-temporal data acquisition as well as capturing images with high frequencies during single flights enables lateral change-detection of moving objects. And if the topography is known, a full recovery of the 3D motion vector is possible.  The underlying idea is the detection or definition of points or areas of interest, which are tracked through consecutive images or frames considering the similarity measures.

In this chapter, pre-processing steps to successful image tracking and vector scaling are introduced. Afterwards, two possible strategies of tracking, i.e. feature-based and patch-based, are explained. Furthermore, different choices of tracking in image sequences are discussed. And finally, examples are given in different fields.

How to cite: Eltner, A., Manfreda, S., Hortobagyi, B., Image Sequences Processing, Unmanned Aerial Vehicles In Environmental Sciences, edited by Eltner, A.; D. Hoffmeister; A. Kaiser, P. Karrasch, L. Klingbeil, C. Stöcker, A. Rovere, (ISBN 978-3-534-40588-6), 260-272, 2022. [PDF]

A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations

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 must be quantified and mitigated. The 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 data preprocessing is 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 static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, 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 selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. 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 an 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 (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. 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 light on details of implementation, 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 be used in order to measure the velocity estimation uncertainty induced by UAS motion.

How to cite: Ljubičić, R., Strelnikova, D., Perks, M. T., Eltner, A., Peña-Haro, S., Pizarro, A., Dal Sasso, S. F., Scherling, U., Vuono, P., and Manfreda, S.: A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations, Hydrol. Earth Syst. Sci., 25, 5105–5132,, 2021. [pdf]