Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland.
Inero Software sp. z o. o., Gdańsk, Poland.
PLoS One. 2023 Sep 15;18(9):e0291595. doi: 10.1371/journal.pone.0291595. eCollection 2023.
In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it became possible to develop bathymetry estimation algorithms that can predict seabed depth and utilize them systematically. Since there are a number of theoretical approaches, physical models, and empirical techniques to use satellite observations in order to estimate depth in the coastal zone, the presented article compares the performance and precision of the most common one to modern machine learning algorithms. More specifically, the models based on shallow neural networks, decision trees and Random Forest algorithms have been proposed, investigated and confronted with the performance of pure analytical models. The particular proposed machine learning models differ also in a set of satellite data bands used as an input as well as in applying or not geographical weighting in the learning process. The obtained results point towards the best performance of the regression tree algorithm that incorporated as inputs information about data localization, raw reflectance data from four satellite data bands and a quotient of logarithms of B2 and B3 bands. The study for the paper was performed in relatively optically difficult and spatially variant conditions of the south Baltic coastline starting at Szczecin, Poland on the west (53°26'17'' N, 14°32'32'' E) to Hel peninsula (54°43'04,3774'' N 18°37'56,9175'' E). The reference bathymetry data was acquired from Polish Marine Administration. It was obtained through profile probing with single-beam sonar or direct in-situ probing.
近年来，对于在海岸线运营的公司和机构来说，关于海底深度的精确和最新信息变得越来越重要。虽然定期进行直接的原位测量，但这些测量既昂贵又耗时，而且在短时间间隔内进行不切实际。与此同时，越来越多的卫星成像数据可用。利用这些图像，开发了可以预测海底深度并系统利用它们的测深估计算法。由于有许多理论方法、物理模型和经验技术可以利用卫星观测来估算沿海地区的水深，因此本文比较了最常见的方法与现代机器学习算法的性能和精度。具体来说，提出、研究并比较了基于浅层神经网络、决策树和随机森林算法的模型，以及基于纯分析模型的性能。所提出的机器学习模型在用作输入的卫星数据波段的集合以及在学习过程中是否应用地理加权方面也有所不同。所得结果表明，回归树算法的性能最佳，该算法将有关数据定位的信息、来自四个卫星数据波段的原始反射率数据以及 B2 和 B3 波段对数的商作为输入。该研究论文是在波兰什切青以西（53°26'17'' N，14°32'32'' E）至赫尔半岛（54°43'04,3774'' N 18°37'56,9175'' E）的南波罗的海海岸线进行的，该地区的光学条件相对困难且空间变化较大。参考水深数据是从波兰海洋管理局获得的。它是通过单波束测深仪或直接原位探测的剖面探测获得的。
