A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations
Само за регистроване кориснике
2021
Аутори
Ljubičić, RobertStrelnikova, Dariia
Perks, Matthew
Eltner, Anette
Peña-Haro, Salvador
Pizarro, Alonso
Dal Sasso, Silvano Fortunato
Scherling, Ulf
Vuono, Pietro
Manfreda, Salvatore
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
unmanned aerial systems / unmanned aerial vehicles / image stabilization / image velocimetryИзвор:
Hydrology and Earth System Sciences, 2021, 25Издавач:
- Copernicus, EGU
Финансирање / пројекти:
- COST Action CA16219; “HARMONIOUS – Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”
Колекције
Институција/група
GraFarTY - 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 . .