@article{Agrafiotis2020,

title = {Correcting Image Refraction: Towards Accurate Aerial Image-Based Bathymetry Mapping in Shallow Waters},

author = {Panagiotis Agrafiotis and Konstantinos Karantzalos and Andreas Georgopoulos and Dimitrios Skarlatos},

url = {https://doi.org/10.3390/rs12020322 https://www.mdpi.com/2072-4292/12/2/322},

doi = {10.3390/rs12020322},

issn = {2072-4292},

year  = {2020},

date = {2020-01-01},

journal = {Remote Sens.},

volume = {12},

number = {2},

pages = {322},

publisher = {MDPI AG},

abstract = {Although aerial image-based bathymetric mapping can provide, unlike acoustic or LiDAR (Light Detection and Ranging) sensors, both water depth and visual information, water refraction poses significant challenges for accurate depth estimation. In order to tackle this challenge, we propose an image correction methodology, which first exploits recent machine learning procedures that recover depth from image-based dense point clouds and then corrects refraction on the original imaging dataset. This way, the structure from motion (SfM) and multi-view stereo (MVS) processing pipelines are executed on a refraction-free set of aerial datasets, resulting in highly accurate bathymetric maps. Performed experiments and validation were based on datasets acquired during optimal sea state conditions and derived from four different test-sites characterized by excellent sea bottom visibility and textured seabed. Results demonstrated the high potential of our approach, both in terms of bathymetric accuracy, as well as texture and orthoimage quality.},

keywords = {Aerial imagery, Bathymetry, Coastal mapping, DSM, Image correction, Machine Learning, Refraction correction, Seabed Mapping, SfM, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2019a,

title = {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},

author = {Panagiotis Agrafiotis and Dimitrios Skarlatos and Andreas Georgopoulos and Konstantinos Karantzalos},

url = {https://www.mdpi.com/2072-4292/11/19/2225},

doi = {10.3390/rs11192225},

issn = {20724292},

year  = {2019},

date = {2019-09-01},

journal = {Remote Sens.},

volume = {11},

number = {19},

pages = {2225},

publisher = {MDPI AG},

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.},

keywords = {Aerial imagery, Bathymetry, Data integration, Fusion, LiDAR, Machine Learning, Point Cloud, Refraction effect, Seabed Mapping, SVM, UAV},

pubstate = {published},

tppubtype = {article}

}