Non-spectral linear depth inversion using drone-acquired wave field imagery

• Published in: Applied ocean research 2023-09, Vol.138, p.103625, Article 103625
• Main Authors: Kim, Byunguk, Noh, Hyoseob, Park, Yong Sung, Lee, Minjae
• Format: Article
• Language: English
• Subjects: Coastal bathymetry; Depth inversion; Ensemble empirical mode decomposition; Large-scale particle image velocimetry; Remote sensing
• ISSN: 0141-1187; 1879-1549
• DOI: 10.1016/j.apor.2023.103625

We present an efficient approach for video-based bathymetry survey. The proposed algorithm consists of preprocessing for drone-acquired video, extracting incident gravity wave, estimating celerity vector field, operating linear water depth inversion, and mapping bathymetry. The image preprocessing includes resizing, masking, barrel distortion correction, ortho-rectification, and georeferencing. The challenges posed by noisy, non-stationary, and irregular wave field data was resolved using ensemble empirical mode decomposition (EEMD), which is capable of extracting incident gravity wave structure without arbitrary initial guessing of frequencies. The extracted wave structures were used to estimate their celerity vector field using large-scale particle image velocimetry (LSPIV) technique. Given that wave celerity depends on water depth in nearshore based on linear dispersion relation, spatial distribution of water depths was determined from wave celerity estimates. The proposed algorithm was tested using the wave field videos and ground truth data, collected during the field experiment at Jangsa beach, located in the east coast of South Korea, and good agreement was achieved.