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Journal Article
Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave fume video images
- Authors
- Jinah Kim, Taekyung Kim, Sang-Ho Oh, Kideok Do, Joon-Gyu Ryu, Jaeil Kim
- Issue Date
- 2021-11
- Citation
- Scientific Reports, v.11, pp.1-12
- ISSN
- 2045-2322
- Publisher
- Nature Research
- Language
- English
- Type
- Journal Article
- Project Code
- 22HH3800, Development of Low Power multiple access core technology for Global IoT Service using Cubesat, Ryu Joon Gyu
- 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 averaged혻correlation 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.
- KSP Keywords
- Acoustic Sensor, Flume experiments, In-Situ, Irregular waves, Multi-view, Nearshore processes, Semi-supervised, Time series, Water level, Wave flume, Wave height
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
