Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data

Abstract

Floating autonomous vehicles are very often equipped with modern systems that collect information about the situation under the water surface, e.g., the depth or type of bottom and obstructions on the seafloor. One such system is the multibeam echosounder (MBES), which collects very large sets of bathymetric data. The development and analysis of such large sets are laborious and expensive. Reduction of the spatial data obtained from bathymetric and other systems collecting spatial data is currently widely used. In commercial programs used in the development of data from hydrographic systems, methods of interpolation to a specific mesh size are very frequently used. The authors of this article previously proposed original the true bathymetric data reduction method (TBDRed) and Optimum Dataset (OptD) reduction methods, which maintain the actual position and depth for each of the measured points, without their interpolation. The effectiveness of the proposed methods has already been presented in previous articles. This article proposes the fusion of original reduction methods, which is a new and innovative approach to the problem of bathymetric data reduction. The article contains a description of the methods used and the methodology of developing bathymetric data. The proposed fusion of reduction methods allows the generation of numerical models that can be a safe, reliable source of information, and a basis for design. Numerical models can also be used in comparative navigation, during the creation of electronic navigation maps and other hydrographic products.

Methodology for Combining Data Acquired by Unmanned Surface and Aerial Vehicles to Create Digital Bathymetric Models in Shallow and Ultra-Shallow Waters

“…In [2], three methods are analyzed, inverse distance weighted (IDW), kriging, and natural neighbor, and IDW is indicated to give the best results. In [53], the proposal of modification dedicated to bathymetric modeling of IDW is proposed, based on bathymetric data, and in [54], the need for big data set reduction prior to interpolation is emphasized. As seen, a variety of methods can be used for interpolation in modeling, with satisfactory results.…”

Sensors and Sensor’s Fusion in Autonomous Vehicles

“…These data are depth points acquired from a multi-beam echo sounder. Wlodarczyk-Sielicka and Blaszczak-Bak deal with the problem of their processing [ 15 ]. A multi-beam echosounder (MBES collects very large sets of bathymetric points—spatial data with location and depth).…”

Testing the Effect of Bathymetric Data Reduction on the Shape of the Digital Bottom Model

“…It also makes it possible to describe and extract relief structures, along with determining their parameters, such as slope, exposure, or relative height [ 32 ]. The creation of a digital terrain model based on data from a multibeam system usually follows the following steps: (1) acquiring a three-dimensional bathymetric dataset; (2) performing pre-processing of the data (including: noise removal, filtering, and data reduction); (3) creating a DTM based on the processed dataset; and (4) analyzing the DTM [ 33 ]. For underwater zone modeling, another name for this is the numerical bottom model (NBM) or the digital bottom model (DBM).…”

“…The use of the available spatial data reduction methods is becoming increasingly common [ 33 ]. The last decades of research on the application of acoustic devices in the water have contributed to the significant development of marine measurement systems.…”