Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images.

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 pe

SUBMITTER: Kim J 

PROVIDER: S-EPMC8571332 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature