![]() ![]() Video systems allow the easy and efficient acquisition of large amounts of data that are highly dense in terms of time and space, in both field and laboratory experiments for investigations however, their applicability depends on the accuracy, reliability, and robustness of processes that can be visually recorded by optical sensors as real physical quantities, compared to in-situ acoustic sensors. Moreover, laboratory environments can also be useful in evaluating the performance of video systems as a measurement tool. Experiments with wave flumes that satisfy scaling laws enable controlled experiments on some nearshore processes, which can yield a variety of data on the parameterization of specific processes. These devices have also been used to measure shoreline positions and infer subsurface morphology as well as to measure the water waves of the inner surf and swash, in addition to sub-aerial bathymetry 2, 3, 4.Īs well as field experiments, laboratory studies should be a component of investigations of nearshore phenomena. ![]() ![]() In particular, land-based remote sensing devices, such as shore-based camera and video systems, enable synoptic surface and subsurface observations with high temporal resolutions over long time scales, even in the case of extreme events 1. The estimated time series were in good agreement within the averaged correlation coefficient of 0.98 and 0.90 on the measurement and 0.95 and 0.85 on the estimation for regular and irregular waves, respectively.Ĭoastal observation using remote sensing and unmanned systems has led to advances in understanding and modeling nearshore processes, such as shorelines, surf zones, and inner shelves, by allowing long-term observation facilities in coastal areas. The performance of the proposed models were evaluated by comparing the estimated time series of water elevation with the ground-truth wave gauge data at three locations along the wave flume. We performed wave flume experiments in a hydraulic laboratory with two cameras with side and top viewpoints to validate the effectiveness of our approach. We also implemented a semi-supervised approach to extract wave height information from long-term sequences of wave height observations with minimal supervision. Specifically, we propose a visual domain adaptation method to build a water level estimator in spite of a situation in which ocean wave height cannot be measured directly. ![]() This paper presents a vision-based water surface elevation estimation approach using multi-view datasets. Accurate water surface elevation estimation is essential for understanding nearshore processes, but it is still challenging due to limitations in measuring water level using in-situ acoustic sensors. ![]()
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