  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.
