A robust filtering algorithm based on the estimation of tracer visibility and stability for large scale particle image velocimetry

Li, Lei; Yan, Hua

doi:10.1016/j.flowmeasinst.2022.102204

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Bodart et al., 2022) and a great amount of the observed errors are due to the lack of tracers (Dal Sasso et al., 2020; Iverson Italo Siebert & Bleninger, 2023; Pumo et al., 2021) and/or the presence of tracers moving with velocity different from the water velocity, such as gravity waves (Dolcetti et al., 2020). But many other filters can be implemented such as the PPSR (Li & Yan, 2022) or the seeding density metrics (Pizarro et al., 2020) for instance. From our observations, a drastic filtering strategy is beneficial to the image velocimetry measurement.…”

Section: Discussionmentioning

confidence: 99%

“…The tracer density throughout the region of interest can be evaluated either with a subjective score or with specific metrics. The metrics could be the image texture as used by Li and Yan (2022), the seeding density metric (Pizarro et al., 2020) or, for LSPIV measurements, the correlation peak width as proposed in this paper. The distribution of the velocity results throughout the region of interest can be evaluated.…”

Section: Discussionmentioning

confidence: 99%

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Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements

Bodart,

Le Coz,

Jodeau

et al. 2024

Water Resources Research

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Operator choices, both in acquiring the video and data and in processing them, can be a prominent source of error in image‐based velocimetry methods applied to river discharge measurements. The Large Scale Particle Image Velocimetry (LSPIV) is known to be sensitive to the parameters and computation choices set by the user, but no systematic comparisons with discharge references or intercomparisons have been conducted yet to evaluate this operator effect in LSPIV. In this paper, an analysis of a video gauging intercomparison, the Video Globe Challenge 2020, is proposed to evaluate such operator effect. The analysis is based on the gauging reports of the 15 to 23 participants using the Fudaa‐LSPIV software and intents to identify the most sensitive parameters for the eight videos. The analysis highlighted the significant impact of the time interval, the grid points and the filters on the LSPIV discharge measurements. These parameters are often inter‐dependent and should be correctly set together to strongly reduce the discharge errors. Based on the results, several automated tools were proposed to reduce the operator effect. These tools consist of several parameter assistants to automatically set the orthorectification resolution, the grid and the time interval, and of a sequence of systematic and automatic filters to ensure reliable velocity measurements used for discharge estimation. The application of the assisted LSPIV workflow using the proposed tools leads to significant improvements of the discharge measurements with strong reductions of the inter‐participant variability. On the eight videos, the mean interquartile range of the discharge errors is reduced from 17% to 5% and the mean discharge bias is reduced from −9% to 1% with the assisted LSPIV workflow. The remaining inter‐participant variability is mainly due to the user‐defined surface velocity coefficient α.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements

Bodart,

Le Coz,

Jodeau

et al. 2024

Water Resources Research

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“…We focused on two cloud‐free and straight river reaches A and B (Figure 1) to reduce computational cost. Image pre‐processing was performed to amplify the visibility of surface tracers with respect to the background (riverbanks/static ground), applying a Contrast‐limited adaptive histogram equalization (CLAHE) filter (with a window size of 8 pixels, matching our smallest IA size, see Section 3.2.1) to enhance image contrast (Li & Yan, 2022; Masafu et al., 2022). Pre‐processing of images for LSPIV has a significant impact on the quality of flow velocity estimates.…”

Section: Methodsmentioning

confidence: 99%

Satellite Video Remote Sensing for Estimation of River Discharge

Masafu,

Williams,

Hurst

2023

Geophysical Research Letters

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We demonstrate that river discharge can be estimated by deriving water surface velocity estimates from satellite‐derived video imagery when combined with high‐resolution topography of channel geometry. Large Scale Particle Image Velocimetry (LSPIV) was used to map surface velocity from 28 s of 5 Hz satellite video acquired at a 1.2 m nominal ground spacing over the Darling River, Tilpa, Australia, during a 1‐in‐5‐year flood. We stabilized and assessed the uncertainty of the residual motion induced by the satellite platform, enhancing our sub‐pixel motion analysis, and quantified the sensitivity of image extraction rates on computed velocities. In the absence of in situ observations, LSPIV velocity estimates were validated against predictions from a calibrated 2D hydrodynamic model. Despite the confounding influence of selecting a surface velocity depth‐averaging coefficient, inference of discharge was within 0.3%–15% compared with gauging station measurements. These results provide a valuable foundation for refining satellite video LSPIV techniques.

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“…Individual frames were extracted from the cropped video at frame rates of 1, 0.5 and 0.25 Hz. Image pre-processing was performed to amplify the visibility of surface tracers with respect to the background (riverbanks/static ground), applying a Contrast-limited adaptive histogram equalization (CLAHE) filter to enhanced image contrast (Li and Yan, 2022;Masafu et al ., 2022). Distinct features on the water surface were difficult to discern in the raw images, which would be expected in natural rivers and given the height of the optical sensor.…”

Section: Large-scale Particle Image Velocimetrymentioning

confidence: 99%