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Open AccessArticle

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

1
Department of Topography, School of Rural and Surveying Engineering, Zografou Campus, National Technical University of Athens, 9 Heroon Polytechniou str., 15780 Athens, Greece
2
Lab of Photogrammetric Vision, Civil Engineering and Geomatics Department, Cyprus University of Technology, 2-8 Saripolou str., 3036 Limassol, Cyprus
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2225; https://doi.org/10.3390/rs11192225
Received: 15 July 2019 / Revised: 5 September 2019 / Accepted: 21 September 2019 / Published: 24 September 2019
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. 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 article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas. View Full-Text
Keywords: point cloud; bathymetry; SVM; machine learning; UAV; aerial imagery; seabed mapping; refraction effect; LiDAR; fusion; data integration point cloud; bathymetry; SVM; machine learning; UAV; aerial imagery; seabed mapping; refraction effect; LiDAR; fusion; data integration
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Agrafiotis, P.; Skarlatos, D.; Georgopoulos, A.; Karantzalos, K. DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds. Remote Sens. 2019, 11, 2225.

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