Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning

Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning

Nearshore bathymetric data are essential for assessing coastal hazards, studying benthic habitats and for coastal engineering. Traditional bathymetry mapping techniques of ship-sounding and airborne LiDAR are laborious, expensive and not always efficient. Multispectral and hyperspectral remote sensi...

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Bibliographic Details
Main Authors: David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland, Steven Michael de Jong
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Remote Sensing
Subjects:
satellite-derived bathymetry
hyperspectral PRISMA
multispectral Sentinel-2
machine learning
convolution neural networks
Online Access:https://www.mdpi.com/2072-4292/17/2/291
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