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Open AccessArticle

Bathymetric Inversion and Uncertainty Estimation from Synthetic Surf-Zone Imagery with Machine Learning

1
Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, 1261 Duck Rd, Duck, NC 27949, USA
2
Department of Earth and Ocean Sciences, University of North Carolina-Wilmington, Wilmington, NC 28405, USA
3
Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Rd, Vicksburg, MS 39180, USA
4
Department of Civil and Environmental Engineering, University of Hawai’i at Mānoa, Honolulu, HI 96822, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3364; https://doi.org/10.3390/rs12203364
Received: 24 August 2020 / Revised: 2 October 2020 / Accepted: 7 October 2020 / Published: 15 October 2020
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
Resolving surf-zone bathymetry from high-resolution imagery typically involves measuring wave speeds and performing a physics-based inversion process using linear wave theory, or data assimilation techniques which combine multiple remotely sensed parameters with numerical models. In this work, we explored what types of coastal imagery can be best utilized in a 2-dimensional fully convolutional neural network to directly estimate nearshore bathymetry from optical expressions of wave kinematics. Specifically, we explored utilizing time-averaged images (timex) of the surf-zone, which can be used as a proxy for wave dissipation, as well as including a single-frame image input, which has visible patterns of wave refraction and instantaneous expressions of wave breaking. Our results show both types of imagery can be used to estimate nearshore bathymetry. However, the single-frame imagery provides more complete information across the domain, decreasing the error over the test set by approximately 10% relative to using timex imagery alone. A network incorporating both inputs had the best performance, with an overall root-mean-squared-error of 0.39 m. Activation maps demonstrate the additional information provided by the single-frame imagery in non-breaking wave areas which aid in prediction. Uncertainty in model predictions is explored through three techniques (Monte Carlo (MC) dropout, infer-transformation, and infer-noise) to provide additional actionable information about the spatial reliability of each bathymetric prediction. View Full-Text
Keywords: machine learning; bathymetry; surf-zone; uncertainty; convolutional; synthetic data machine learning; bathymetry; surf-zone; uncertainty; convolutional; synthetic data
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MDPI and ACS Style

Collins, A.M.; Brodie, K.L.; Bak, S.A.; Hesser, T.J.; Farthing, M.W.; Lee, J.; Long, J.W. Bathymetric Inversion and Uncertainty Estimation from Synthetic Surf-Zone Imagery with Machine Learning. Remote Sens. 2020, 12, 3364.

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