Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14096
Title: Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning
Authors: Agrafiotis, Panagiotis 
Karantzalos, K. 
Georgopoulos, Andreas 
Skarlatos, Dimitrios 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Bathymetry;Machine Learning;Point Cloud;Refraction effect;Seabed Mapping;SVM;UAV
Issue Date: 17-Apr-2019
Source: Underwater 3D Recording and Modelling ""A Tool for Modern Applications and CH Recording"", Limassol, Cyprus, 2 May 2019 through 3 May 2019
Volume: 42
Issue: 2/W10
Journal: ISPRS Journal of Photogrammetry and Remote Sensing 
Conference: Underwater 3D Recording and Modelling 
Abstract: The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.
Description: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Volume 42, Issue 2/W10, 17 April 2019, Pages 9-16
ISSN: 2194-9042
DOI: 10.5194/isprs-archives-XLII-2-W10-9-2019
Rights: © Author(s) . This work is distributed under the Creative Commons Attribution 4.0 License
Type: Article
Affiliation : National Technical University of Athens 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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