Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC

Abstract

Timely observations of nearshore water depths are important for a variety of coastal research and management topics, yet this information is expensive to collect using in situ survey methods. Remote methods to estimate bathymetry from imagery include using either ratios of multi-spectral reflectance bands or inversions from wave processes. Multi-spectral methods work best in waters with low turbidity, and wave-speed-based methods work best when wave breaking is minimal. In this work, we build on the wave-based inversion approaches, by exploring the use of a fully convolutional neural network (FCNN) to infer nearshore bathymetry from imagery of the sea surface and local wave statistics. We apply transfer learning to adapt a CNN originally trained on synthetic imagery generated from a Boussinesq numerical wave model to utilize tower-based imagery collected in Duck, North Carolina, at the U.S. Army Engineer Research and Development Center’s Field Research Facility. We train the model on sea-surface imagery, wave conditions, and associated surveyed bathymetry using three years of observations, including times with significant wave breaking in the surf zone. This is the first time, to the authors’ knowledge, an FCNN has been successfully applied to infer bathymetry from surf-zone sea-surface imagery. Model results from a separate one-year test period generally show good agreement with survey-derived bathymetry (0.37 m root-mean-squared error, with a max depth of 6.7 m) under diverse wave conditions with wave heights up to 3.5 m. Bathymetry results quantify nearshore bathymetric evolution including bar migration and transitions between single- and double-barred morphologies. We observe that bathymetry estimates are most accurate when time-averaged input images feature visible wave breaking and/or individual images display wave crests. An investigation of activation maps, which show neuron activity on a layer-by-layer basis, suggests that the model is responsive to visible coherent wave structures in the input images.

Keywords

• machine learning
• computer vision
• fully convolutional neural network
• bathymetry inversion
• coastal
• image-based