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

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

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 ex...

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Bibliographic Details
Main Authors: Adam M. Collins, Katherine L. Brodie, Andrew Spicer Bak, Tyler J. Hesser, Matthew W. Farthing, Jonghyun Lee, Joseph W. Long
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Remote Sensing
Subjects:
machine learning
bathymetry
surf-zone
uncertainty
convolutional
synthetic data
Online Access:https://www.mdpi.com/2072-4292/12/20/3364

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