Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/22784
Title: Learning from Synthetic Data: Enhancing Refraction Correction Accuracy for Airborne Image-Based Bathymetric Mapping of Shallow Coastal Waters
Authors: Agrafiotis, Panagiotis 
Karantzalos, Konstantinos 
Georgopoulos, Andreas 
Skarlatos, Dimitrios 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Airborne;Bathymetry;Coastal mapping;Machine learning;Refraction correction;Shallow waters;Support vector regression;Synthetic data;UAV
Issue Date: Apr-2021
Source: PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2021, vol. 89, no. 2, pp. 91 - 109
Volume: 89
Issue: 2
Start page: 91
End page: 109
Journal: PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science 
Abstract: The increasing need for accurate bathymetric mapping is essential for a plethora of offshore activities. Even though aerial image datasets through Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques can provide a low-cost alternative compared to LiDAR and SONAR, offering additionally, important visual information, water refraction poses significant obstacles in delivering accurate bathymetry. In this article, the generation of manned and unmanned airborne synthetic datasets of dry and water covered areas is presented. These data are used to train models for correcting the geometric effects of refraction on real-world image-based point clouds and aerial images. Based on a thorough evaluation, important improvements are presented, indicating the increased accuracy and the reduced noise in the point clouds of the derived bathymetric products, meeting also the International Hydrographic Organization’s (IHO) standards.
URI: https://hdl.handle.net/20.500.14279/22784
ISSN: 25122819
DOI: 10.1007/s41064-021-00144-1
Rights: © Springer Nature
Type: Article
Affiliation : National Technical University Of Athens 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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