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Probabilistic Evaluation and Filtering of Image Velocimetry Measurements
The recent technological advances in remote sensing (e.g., unmanned aerial vehicles, digital image acquisition, etc.) have vastly improved the applicability of image velocimetry in hydrological studies. Thus, image velocimetry has become an established technique with an acceptable error for practical applications (the error can be lower than 10%). The main source of errors has been attributed to incomplete intrinsic and extrinsic camera calibration, to non-constant frame rate and to spurious low velocities due to moving objects that are irrelevant to the streamflow. Some researchers have even employed probabilistic approaches (Monte Carlo simulations) to analyze the uncertainty introduced during the camera calibration procedure. On the other hand, the endogenous uncertainty of the image velocimetry algorithms per se has received little attention. In this study, a probabilistic approach is employed to systematically analyze this uncertainty. It is argued that this analysis may not only improve the performance of the image velocimetry methods but it can also provide information regarding the impact of the video recording conditions (e.g., low density of features, oblique camera angle, low resolution, etc.) on the accuracy of the estimated values. The suggested method has been tested in six case studies of which the data have been previously made publicly available by independent researchers.
Probabilistic Evaluation and Filtering of Image Velocimetry Measurements
The recent technological advances in remote sensing (e.g., unmanned aerial vehicles, digital image acquisition, etc.) have vastly improved the applicability of image velocimetry in hydrological studies. Thus, image velocimetry has become an established technique with an acceptable error for practical applications (the error can be lower than 10%). The main source of errors has been attributed to incomplete intrinsic and extrinsic camera calibration, to non-constant frame rate and to spurious low velocities due to moving objects that are irrelevant to the streamflow. Some researchers have even employed probabilistic approaches (Monte Carlo simulations) to analyze the uncertainty introduced during the camera calibration procedure. On the other hand, the endogenous uncertainty of the image velocimetry algorithms per se has received little attention. In this study, a probabilistic approach is employed to systematically analyze this uncertainty. It is argued that this analysis may not only improve the performance of the image velocimetry methods but it can also provide information regarding the impact of the video recording conditions (e.g., low density of features, oblique camera angle, low resolution, etc.) on the accuracy of the estimated values. The suggested method has been tested in six case studies of which the data have been previously made publicly available by independent researchers.
Probabilistic Evaluation and Filtering of Image Velocimetry Measurements
Evangelos Rozos (author) / Katerina Mazi (author) / Antonis D. Koussis (author)
2021
Article (Journal)
Electronic Resource
Unknown
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