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The increasing need for accurate bathymetric mapping is essential for a plethora of offshore activities. Even though aerial image datasets through Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques can provide a low-cost alternative compared to LiDAR and SONAR, offering additionally, important visual information, water refraction poses significant obstacles in delivering accurate bathymetry. In this article, the generation of manned and unmanned airborne synthetic datasets of dry and water covered areas is presented. These data are used to train models for correcting the geometric effects of refraction on real-world image-based point clouds and aerial images. Based on a thorough evaluation, important improvements are presented, indicating the increased accuracy and the reduced noise in the point clouds of the derived bathymetric products, meeting also the International Hydrographic Organization’s (IHO) standards.

During the past few years there was an increasing demands of needs for mapping the bottom of water, either because it was needed for Navigation Safety, Nautical charts, or for Pollution controlling, mineral and fish industries. Over the years the methods of bathymetry and mapping showed a huge improvement especially in the last 40 years where a rapid growth occurs in this part of science. Particular growth occurs in the bathymetry area using satellite data which continuously presented and exported new models in order to create maps in a shorter time period and with fewer expenses. Additionally there is an increasing improvement in the accuracy of the results of the maps over time and it is accomplished with smaller errors. For export of maps and finding data followed a fairly complicated process which needs to take into account many parameters are either located in the constituents of water, either the nature of the water bottom, or in the atmosphere and beyond. The method of remote sensing is divided into two main categories imagine methods and the Non imagine methods. Both are widely known, in conclusion, the methodology of remote sensing comparatively with the eco-sounding method is more efficient in a matter of time, financial budget, data accuracy than any other method exists, and usually is recommended for use. Πάντα υπήρχε η ανάγκη της χαρτογράφησης του πυθμένα του νερού, είτε ο λόγος αυτός αφορούσε την ασφαλή ναυσιπλοία είτε αφορούσε τον έλεγχο της στάθμης του νερού ή αφορούσε περιβαλλοντικούς λόγους. Κατά την πάροδο των χρόνων οι μέθοδοι της βυθομέτρησης και της χαρτογράφησης εξελίσονταν και βελτιώνονταν, ειδικά τα τελευταία 40 χρόνια όπου παρουσιάζεται μία ραγδαία ανάπτυξη στον τομέα αυτό. Ιδιαίτερη ανάπτυξη παρουσιάζεται στον τομέα της βυθομετρίας με την χρήση δορυφορικών δεδομένων όπου συνεχώς παρουσιάζονται νέοι τρόποι εξαγωγής χαρτών σε συντομότερο χρόνο και με λιγότερες δαπάνες. Επιπρόσθετα διακρίνεται μία συνεχής βελτίωση στην ακρίβεια των αποτελεσμάτων καθώς με τον καιρό επιτυγχάνεται η εξαγωγή αποτελεσμάτων με μεγαλύτερες ακρίβειες συνεπώς με μικρότερα σφάλματα. Για την εξαγωγή των χαρτών και την εύρεση των δεδομένων ακολουθείται μία αρκετά περίπλοκη διαδικασία όπου χρειάζεται να λαμβάνονται υπόψη πολλοί παραμέτροι είτε αυτοί βρίσκονται στα συστατικά του νερού, είτε στο είδος του πυθμένα του νερού, είτε στην ατμόσφαιρα και όχι μόνο. Η μέθοδος της τηλεπισκόπησης διακρίνεται σε δύο βασικές κατηγορίες η μέθοδος απικόνησης και η μέθοδος μη απικόνησης. Είναι και οι δύο ευρέως γνωστές. Εν κατακλείδι, η μέθοδολογία της τηλεπισκόπησης είναι πιο αποδοτική σε θέμα χρόνου, οικονομικού προυπολογισμού, ακρίβεια δεδομένων σε σχέση με οποιαδήποτε άλλη μέθοδο υπάρχει, και συνήθως είναι αυτή που συστήνεται για χρήση. Completed

The submerged harbor of Amathus in Cyprus is a sensitive cultural heritage requiring special attention in the frame of Marine Spatial Planning. The monitoring of water depth in the surrounding area can raise awareness on effects, such as shoreline erosion, which could lead to a deterioration of the relics. This paper assesses the quality of bathymetric maps around the site derived from the DESIS hyperspectral sensor mounted on the International Space Station. The depth values are compared to products derived from traditional multispectral sensors, and assessed with LiDAR measurements acquired in situ. An imaging spectrometer such as DESIS would be able to derive additional water quality parameter such as phytoplankton concentration, assessing at the same time eutrophication and pollution in this sensitive area.

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.

During the last years, many studies related to Satellite-Derived Bathymetry (SDB) emphasize the potential use of optical satellite remote sensing sensors for bathymetric estimation. For this study, ten multispectral SPOT 6/7 satellite images with a medium resolution covering the coastal waters of the study areas were analyzed. These images were geometric, radiometric, and atmospheric corrected and acquired in three different sensing dates having coverage with at least 30% of lidar data. A number of 5284 random depth measurements with 0 to 50 meters depth were acquired for the ratio conversion algorithm with absolute depths and error assessment. A series of steps were performed to obtain reliable results using satellite optical data such as, sun glint process, land/sea extraction, kernel filters. The study area was divided into three sub-regions, based on the sensing date of the satellite imageries. The light attenuation in the water column increases at a depth of about thirty meters as seen in other related studies. This study identified the depth of light attenuation to determine the maximum depth that can be estimated through optical sensors. The results show that better correlation was identified up to 15 meters depth. Results of the regression analysis show the following correlation coefficients R² :0.90, 0.87, 0.80, and 0.89 with the Root Mean Square Error (RMSE) for the respective study areas to be 1.34, 1.53 1.70 and, 1.15.