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A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images

Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion

Laboratory of Geophysics-Satellite Remote Sensing & Archaeoenvironment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies (IMS), Foundation for Research & Technology, Hellas, Nikiforou Foka 130 & Melissinou, P.O. Box 119, 74100 Rethymno, Crete, Greece
Author to whom correspondence should be addressed.
Academic Editors: Panagiotis Agrafiotis, Gema Casal, Gottfried Mandlburger, Karantzalos Konstantinos and Dimitrios Skarlatos
Remote Sens. 2022, 14(5), 1127;
Received: 22 December 2021 / Revised: 15 February 2022 / Accepted: 18 February 2022 / Published: 24 February 2022
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets with centimeter resolution. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery as an alternative to costly hyperspectral sensors for drones. Combining drone-based RGB and multispectral imagery into a single cube dataset provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require the input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types with a maximum depth of six meters. The use of suitable end-member spectra, which are representative of the seafloor types of the study area, are important parameters in model tuning. The results of this study are promising, showing good correlation (R2 > 0.75 and Lin’s coefficient > 0.80) and less than half a meter average error when they are compared with sonar depth measurements. Consequently, the integration of imagery from various drone-based sensors (visible range) assists in producing detailed bathymetry maps for small-scale shallow areas based on optical modelling. View Full-Text
Keywords: drones; UAV; bathymetry; shallow water; multispectral; inversion drones; UAV; bathymetry; shallow water; multispectral; inversion
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MDPI and ACS Style

Alevizos, E.; Oikonomou, D.; Argyriou, A.V.; Alexakis, D.D. Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion. Remote Sens. 2022, 14, 1127.

AMA Style

Alevizos E, Oikonomou D, Argyriou AV, Alexakis DD. Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion. Remote Sensing. 2022; 14(5):1127.

Chicago/Turabian Style

Alevizos, Evangelos, Dimitrios Oikonomou, Athanasios V. Argyriou, and Dimitrios D. Alexakis. 2022. "Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion" Remote Sensing 14, no. 5: 1127.

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