Version 1
: Received: 17 June 2024 / Approved: 17 June 2024 / Online: 19 June 2024 (13:31:34 CEST)
How to cite:
Makris, A.; Nicodemou, V. C.; Alevizos, E.; Oikonomidis, I.; D. Alexakis, D.; Roussos, A. Refraction-aware Structure-from-Motion for Airborne Bathymetry. Preprints2024, 2024061178. https://doi.org/10.20944/preprints202406.1178.v1
Makris, A.; Nicodemou, V. C.; Alevizos, E.; Oikonomidis, I.; D. Alexakis, D.; Roussos, A. Refraction-aware Structure-from-Motion for Airborne Bathymetry. Preprints 2024, 2024061178. https://doi.org/10.20944/preprints202406.1178.v1
Makris, A.; Nicodemou, V. C.; Alevizos, E.; Oikonomidis, I.; D. Alexakis, D.; Roussos, A. Refraction-aware Structure-from-Motion for Airborne Bathymetry. Preprints2024, 2024061178. https://doi.org/10.20944/preprints202406.1178.v1
APA Style
Makris, A., Nicodemou, V. C., Alevizos, E., Oikonomidis, I., D. Alexakis, D., & Roussos, A. (2024). Refraction-aware Structure-from-Motion for Airborne Bathymetry. Preprints. https://doi.org/10.20944/preprints202406.1178.v1
Chicago/Turabian Style
Makris, A., Dimitrios D. Alexakis and Anastasios Roussos. 2024 "Refraction-aware Structure-from-Motion for Airborne Bathymetry" Preprints. https://doi.org/10.20944/preprints202406.1178.v1
Abstract
In this work, we introduce the first pipeline that combines a refraction-aware Structure-from-Motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach the real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements on accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at 2 locations in the southern Mediterranean Sea, with varying seafloor types which demonstrate the benefits of refraction correction for the deep learning framework. We open-source our refraction-aware SfM, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.
Keywords
refraction-aware Structure-from-Motion; deep learning; shallow water bathymetry; drone imagery
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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