TY  - JOUR
AU  - Ljubičić, Robert
AU  - Strelnikova, Dariia
AU  - Perks, Matthew
AU  - Eltner, Anette
AU  - Peña-Haro, Salvador
AU  - Pizarro, Alonso
AU  - Dal Sasso, Silvano Fortunato
AU  - Scherling, Ulf
AU  - Vuono, Pietro
AU  - Manfreda, Salvatore
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2413
AB  - 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.
PB  - Copernicus, EGU
T2  - Hydrology and Earth System Sciences
T1  - A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations
VL  - 25
DO  - 10.5194/hess-25-5105-2021
ER  - 

@article{
author = "Ljubičić, Robert and Strelnikova, Dariia and Perks, Matthew and Eltner, Anette and Peña-Haro, Salvador and Pizarro, Alonso and Dal Sasso, Silvano Fortunato and Scherling, Ulf and Vuono, Pietro and Manfreda, Salvatore",
year = "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 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.",
publisher = "Copernicus, EGU",
journal = "Hydrology and Earth System Sciences",
title = "A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations",
volume = "25",
doi = "10.5194/hess-25-5105-2021"
}

Ljubičić, R., Strelnikova, D., Perks, M., Eltner, A., Peña-Haro, S., Pizarro, A., Dal Sasso, S. F., Scherling, U., Vuono, P.,& Manfreda, S.. (2021). A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations. in Hydrology and Earth System Sciences
Copernicus, EGU., 25.
https://doi.org/10.5194/hess-25-5105-2021

Ljubičić R, Strelnikova D, Perks M, Eltner A, Peña-Haro S, Pizarro A, Dal Sasso SF, Scherling U, Vuono P, Manfreda S. A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations. in Hydrology and Earth System Sciences. 2021;25.
doi:10.5194/hess-25-5105-2021 .

Ljubičić, Robert, Strelnikova, Dariia, Perks, Matthew, Eltner, Anette, Peña-Haro, Salvador, Pizarro, Alonso, Dal Sasso, Silvano Fortunato, Scherling, Ulf, Vuono, Pietro, Manfreda, Salvatore, "A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations" in Hydrology and Earth System Sciences, 25 (2021),
https://doi.org/10.5194/hess-25-5105-2021 . .

TY  - JOUR
AU  - Pearce, Sophie
AU  - Ljubičić, Robert
AU  - Peña-Haro, Salvador
AU  - Perks, Matthew
AU  - Tauro, Flavia
AU  - Pizarro, Alonso
AU  - Dal Sasso, Silvano
AU  - Strelnikova, Dariia
AU  - Grimaldi, Salvatore
AU  - Maddock, Ian
AU  - Paulus, Gernot
AU  - Plavšić, Jasna
AU  - Prodanović, Dušan
AU  - Manfreda, Salvatore
PY  - 2020
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1869
AB  - Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s−1, Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s−1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s−1 of the ADCP measurements, on average.
PB  - MDPI
T2  - Remote Sensing
T1  - An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems
IS  - 2
VL  - 12
DO  - 10.3390/rs12020232
ER  - 

@article{
author = "Pearce, Sophie and Ljubičić, Robert and Peña-Haro, Salvador and Perks, Matthew and Tauro, Flavia and Pizarro, Alonso and Dal Sasso, Silvano and Strelnikova, Dariia and Grimaldi, Salvatore and Maddock, Ian and Paulus, Gernot and Plavšić, Jasna and Prodanović, Dušan and Manfreda, Salvatore",
year = "2020",
abstract = "Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s−1, Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s−1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s−1 of the ADCP measurements, on average.",
publisher = "MDPI",
journal = "Remote Sensing",
title = "An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems",
number = "2",
volume = "12",
doi = "10.3390/rs12020232"
}

Pearce, S., Ljubičić, R., Peña-Haro, S., Perks, M., Tauro, F., Pizarro, A., Dal Sasso, S., Strelnikova, D., Grimaldi, S., Maddock, I., Paulus, G., Plavšić, J., Prodanović, D.,& Manfreda, S.. (2020). An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. in Remote Sensing
MDPI., 12(2).
https://doi.org/10.3390/rs12020232

Pearce S, Ljubičić R, Peña-Haro S, Perks M, Tauro F, Pizarro A, Dal Sasso S, Strelnikova D, Grimaldi S, Maddock I, Paulus G, Plavšić J, Prodanović D, Manfreda S. An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. in Remote Sensing. 2020;12(2).
doi:10.3390/rs12020232 .

Pearce, Sophie, Ljubičić, Robert, Peña-Haro, Salvador, Perks, Matthew, Tauro, Flavia, Pizarro, Alonso, Dal Sasso, Silvano, Strelnikova, Dariia, Grimaldi, Salvatore, Maddock, Ian, Paulus, Gernot, Plavšić, Jasna, Prodanović, Dušan, Manfreda, Salvatore, "An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems" in Remote Sensing, 12, no. 2 (2020),
https://doi.org/10.3390/rs12020232 . .