Kim Jinah, Kim Taekyung, Oh Sang-Ho, Do Kideok, Ryu Joon-Gyu, Kim Jaeil
Coastal Disaster Research Center, Korea Institute of Ocean Science and Technology, Busan, 49111, South Korea.
Department of Civil Engineering, Changwon National University, Changwon-si, 51140, South Korea.
Sci Rep. 2021 Nov 5;11(1):21776. doi: 10.1038/s41598-021-01157-x.
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.
准确估计水面高程对于理解近岸过程至关重要，但由于使用现场声学传感器测量水位存在局限性，这仍然具有挑战性。本文提出了一种使用多视图数据集的基于视觉的水面高程估计方法。具体而言，我们提出了一种视觉域适应方法，以构建一个水位估计器，尽管存在无法直接测量海浪波高的情况。我们还实施了一种半监督方法，以最少的监督从长期波高观测序列中提取波高信息。我们在水力实验室中使用两台具有侧面和顶部视角的相机进行了波浪水槽实验，以验证我们方法的有效性。通过将估计的水位时间序列与波浪水槽沿线三个位置的地面真值波浪测量仪数据进行比较，评估了所提出模型的性能。对于规则波和不规则波，估计的时间序列分别在测量时平均相关系数为0.98和0.90以及估计时平均相关系数为0.95和0.85的情况下具有良好的一致性。
