Next Article in Journal
Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model
Previous Article in Journal
The Inter-Calibration of the DSCOVR EPIC Imager with Aqua-MODIS and NPP-VIIRS
Article Menu
Issue 13 (July-1) cover image

Export Article

Open AccessArticle

The Reduction Method of Bathymetric Datasets that Preserves True Geodata

1
Institute of Geoinformatics, Department of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland
2
Department of Geodesy, Gdansk University of Technology, 80-233 Gdansk, Poland
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1610; https://doi.org/10.3390/rs11131610
Received: 29 May 2019 / Revised: 26 June 2019 / Accepted: 3 July 2019 / Published: 6 July 2019
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Environment)
  |  
PDF [8688 KB, uploaded 11 July 2019]
  |  

Abstract

Water areas occupy over 70 percent of the Earth’s surface and are constantly subject to research and analysis. Often, hydrographic remote sensors are used for such research, which allow for the collection of information on the shape of the water area bottom and the objects located on it. Information about the quality and reliability of the depth data is important, especially during coastal modelling. In-shore areas are liable to continuous transformations and they must be monitored and analyzed. Presently, bathymetric geodata are usually collected via modern hydrographic systems and comprise very large data point sequences that must then be connected using long and laborious processing sequences including reduction. As existing bathymetric data reduction methods utilize interpolated values, there is a clear requirement to search for new solutions. Considering the accuracy of bathymetric maps, a new method is presented here that allows real geodata to be maintained, specifically position and depth. This study presents a description of a developed method for reducing geodata while maintaining true survey values. View Full-Text
Keywords: big data applications; data processing; data visualization; neural networks; reduction; coastal waters big data applications; data processing; data visualization; neural networks; reduction; coastal waters
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Wlodarczyk-Sielicka, M.; Stateczny, A.; Lubczonek, J. The Reduction Method of Bathymetric Datasets that Preserves True Geodata. Remote Sens. 2019, 11, 1610.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top