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, 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.
Highlights
Through-water depth determination from aerial imagery is a much more time consuming and costly process compared to similar onshore mapping tasks
In order to evaluate the performance of the developed model in terms of robustness and effectiveness, seven different training sets were formed from the three test sites of different seabed characteristics and validated against 28 different testing sets
A model trained on a test site, was tested on many other different test sites where LiDAR data were available but used only for evaluating the accuracy of the predicted depths
Summary
Through-water depth determination from aerial imagery is a much more time consuming and costly process compared to similar onshore mapping tasks. Using imagery data, a permanent record of other features can be obtained in the coastal region, such as tidal levels, coastal dunes, rock platforms, beach erosion, and vegetation [2]. This is the case, even though many alternatives for bathymetry [3] have been reported recently.
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