Marine Technology Ltd., 81-521 Gdynia, Poland.
Faculty of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland.
Sensors (Basel). 2023 Jun 8;23(12):5445. doi: 10.3390/s23125445.
Depth data and the digital bottom model created from it are very important in the inland and coastal water zones studies and research. The paper undertakes the subject of bathymetric data processing using reduction methods and examines the impact of data reduction according to the resulting representations of the bottom surface in the form of numerical bottom models. Data reduction is an approach that is meant to reduce the size of the input dataset to make it easier and more efficient for analysis, transmission, storage and similar. For the purposes of this article, test datasets were created by discretizing a selected polynomial function. The real dataset, which was used to verify the analyzes, was acquired using an interferometric echosounder mounted on a HydroDron-1 autonomous survey vessel. The data were collected in the ribbon of Lake Klodno, Zawory. Data reduction was conducted in two commercial programs. Three equal reduction parameters were adopted for each algorithm. The research part of the paper presents the results of the conducted analyzes of the reduced bathymetric datasets based on the visual comparison of numerical bottom models, isobaths, and statistical parameters. The article contains tabular results with statistics, as well as the spatial visualization of the studied fragments of numerical bottom models and isobaths. This research is being used in the course of work on an innovative project that aims to develop a prototype of a multi-dimensional and multi-temporal coastal zone monitoring system using autonomous, unmanned floating platforms at a single survey pass.
水深数据及其生成的数字海底模型在内陆和沿海水域的研究中非常重要。本文采用降维方法处理水深数据,并根据数字海底模型中海底表面的表示形式,检查数据降维的影响。数据降维是一种旨在减小输入数据集大小的方法,以便于分析、传输、存储和类似操作。为此,本文通过离散化选定的多项式函数创建了测试数据集。实际数据集是使用安装在 HydroDron-1 自主测量船的干涉回声测深仪采集的,用于验证分析。数据是在 Zawory 的 Klodno 湖的带状区域中采集的。数据降维是在两个商业程序中进行的。每个算法采用三个相等的降维参数。本文的研究部分介绍了基于数字海底模型、等深线和统计参数的视觉比较对降维水深数据集进行的分析结果。本文包含带有统计信息的表格结果,以及研究的数字海底模型和等深线片段的空间可视化。这项研究用于一个创新项目的工作中,该项目旨在使用自主、无人漂浮平台在单次测量中开发多维、多时相沿海监测系统的原型。