Optical flow algorithm as estimator of horizontal discrepancy between features derived from DEMs: rivers and creeks as case study

Reinoso, J. F.; León, Carlos; Mataix, J.

doi:10.1179/1752270613y.0000000073

Cited by 2 publications

(7 citation statements)

References 11 publications

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“…The two last columns show the mean absolute (ABS) error for each place and the total mean ABS (error). The mean ABS (error) shown in Table 5 seems somewhat high, particularly if compared with the estimations obtained using the optical flow algorithm [8] which had a mean error of 1.13 m. Nevertheless, the optical flow algorithm features a parameter (α) that is similar to a scale factor. For this reason, if we find a suitable scale factor that reduces the errors then PIVEHD can be considered as scalable and a good estimator of displacement.…”

Section: Results Analysismentioning

confidence: 85%

“…In order to test the ability of PIV in estimating the horizontal displacement between two DEMs, we adopted the dataset used in [8]. It consists of four samples (corresponding to four different places) pertaining to terrains with different slopes; they are labeled as upland, hill and mountain according to the classification from [17].…”

Section: Materials and Methodologymentioning

confidence: 99%

“…Just the contour-based [7] and optical flow [8] approaches study the DEM horizontal variable. So we consider it necessary to compare the performance between both algorithms, and such a performance is studied considering the absolute error between the ground truth and the estimation achieved by every approach (contour, optical flow and PIV).…”

Section: Performance In Comparison To Previous Approachesmentioning

confidence: 99%

“…There are no reports to date of horizontal accuracy of DTMs or DEMs linked with the derived features, most likely because any such measure, if it exists, would hardly be applicable to other features [5]. More recently, algorithms were defined to link the differences between horizontal accuracy and the derived parameters of features; such algorithms could be based on a triangulated irregular network (TIN) [6,7] or DEM grid models [8]. The first approach generalizes the separation between homologous contours as the measure of horizontal discrepancy for the whole DEM, whereas the second compares several cells in two or more DEMs to estimate the horizontal discrepancy.…”

Section: Introductionmentioning

confidence: 99%

“…In our present study we use an approach similar to that of [8], but apply the particle image velocimetry (PIV) algorithm. PIV comes from a distant research field, more commonly used in studies involving aerodynamics [13], transonic flows [14], wind tunnels [15], or aeroacoustic predictions [16].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Horizontal Displacement between DEMs by Means of Particle Image Velocimetry Techniques

2015

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To date, digital terrain model (DTM) accuracy has been studied almost exclusively by computing its height variable. However, the largely ignored horizontal component bears a great influence on the positional accuracy of certain linear features, e.g., in hydrological features. In an effort to fill this gap, we propose a means of measurement different from the geomatic approach, involving fluid mechanics (water and air flows) or aerodynamics. The particle image velocimetry (PIV) algorithm is proposed as an estimator of horizontal differences between digital elevation models (DEM) in grid format. After applying a scale factor to the displacement estimated by the PIV algorithm, the mean error predicted is around one-seventh of the cell size of the DEM with the greatest spatial resolution, and around one-nineteenth of the cell size of the DEM with the least spatial resolution. Our methodology allows all kinds of DTMs to be compared once they are transformed into DEM format, while also allowing comparison of data from diverse capture methods, i.e., LiDAR versus photogrammetric data sources.

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Section: Results Analysismentioning

confidence: 85%

Section: Materials and Methodologymentioning

confidence: 99%

Section: Performance In Comparison To Previous Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Horizontal Displacement between DEMs by Means of Particle Image Velocimetry Techniques

2015

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Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

et al. 2018

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Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7-8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

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Optical flow algorithm as estimator of horizontal discrepancy between features derived from DEMs: rivers and creeks as case study

Cited by 2 publications

References 11 publications

Estimating Horizontal Displacement between DEMs by Means of Particle Image Velocimetry Techniques

Estimating Horizontal Displacement between DEMs by Means of Particle Image Velocimetry Techniques

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

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