Abstract
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. This paper presents a vision-based water surface elevation estimation approach using multi-view datasets. 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. We also implemented a semi-supervised approach to extract wave height information from long-term sequences of wave height observations with minimal supervision. We performed wave flume experiments in a hydraulic laboratory with two cameras with side and top viewpoints to validate the effectiveness of our approach. 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. 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.
Highlights
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
Coastal 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
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; 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
Summary
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. 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] These devices have 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 b athymetry[2,3,4]. 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; 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. Improvements, often achieving a high level of accuracy in perceiving precisely real-world objects, which has resulted in a paradigm shift in the field
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