@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}

}

@article{Agrafiotis2019,

title = {SHALLOW WATER BATHYMETRY MAPPING from UAV IMAGERY BASED on MACHINE LEARNING},

author = {P Agrafiotis and D Skarlatos and A Georgopoulos and K Karantzalos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W10/9/2019/},

doi = {10.5194/isprs-archives-XLII-2-W10-9-2019},

issn = {21949050},

year  = {2019},

date = {2019-04-01},

journal = {ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.},

volume = {42},

number = {2/W10},

pages = {9--16},

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.},

keywords = {Bathymetry, Machine Learning, Point Cloud, Refraction effect, Seabed Mapping, SVM, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Tapinaki2019,

title = {3D IMAGE BASED GEOMETRIC DOCUMENTATION OF A MEDIEVAL FORTRESS},

author = {S Tapinaki and M Skamantzari and R Chliverou and V Evgenikou and A M Konidi and E Ioannatou and A Mylonas and A Georgopoulos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W9/699/2019/},

doi = {10.5194/isprs-archives-XLII-2-W9-699-2019},

issn = {2194-9034},

year  = {2019},

date = {2019-01-01},

journal = {ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.},

volume = {XLII-2/W9},

pages = {699--705},

abstract = {Abstract. The detailed and thorough documentation of monuments is a rather complex process that requires the application of the best available state of the art techniques in order to preserve, restore, promote and make cultural heritage accessible to the public. This paper presents the 3D Geometric Documentation of a part of the medieval fortress of Chios, focussing in particular on the practical challenges which the object presented. The case study is a part of the fortified construction, consisting of a bastion, a watch tower on top of this bastion and a significant part of its walls with a surface of about 1053m2 in total. The goal of the survey was to produce an accurate 3D detailed textured model and a series of coloured orthophotos and 2D vector drawings. The documentation methods employed included close-range automated photogrammetry and image-based modelling, terrestrial laser scanning and topographic surveys, an ideal combination of methods.},

keywords = {3D model, laser scanning, Orthophoto, Photogrammetry, UAV},

pubstate = {published},

tppubtype = {article}

}