Department of Geodesy and Offshore Survey, Maritime University of Szczecin, Żołnierska 46, 71-250 Szczecin, Poland.
Department of Computer Engineering, Koszalin University of Technology, Sniadeckich 2, 75-453 Koszalin, Poland.
Sensors (Basel). 2022 Apr 19;22(9):3121. doi: 10.3390/s22093121.
An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing the asymmetric amount of data representing these three groups. A total of 53 Synthetic Minority Oversampling Technique (SMOTE) algorithms were adjusted and evaluated to balance the amount of data. The prepared data set was used to train the Multi-Layer Perceptron (MLP) neural network used for classifying the point cloud. Data balancing contributed to significantly increasing the accuracy of classification. The best overall classification accuracy achieved varied from 95.8% to 97.0%, depending on the oversampling algorithm used, and was significantly better than the classification accuracy obtained for unbalanced data and data with downsampling (89.6% and 93.5%, respectively). Some of the algorithms allow for 10% increased detection of points on the objects compared to unbalanced data or data with simple downsampling. The results suggest that the use of selected oversampling algorithms can aid in improving the point cloud classification and making the airborne laser bathymetry technique more appropriate for seabed mapping.
海底航空测绘中一个重要的问题是准确分类表征水面、水底和水底物体的点云。本研究旨在通过解决代表这三组数据量不对称的问题来提高分类精度。总共调整和评估了 53 种合成少数过采样技术 (SMOTE) 算法来平衡数据量。准备好的数据集用于训练用于分类点云的多层感知器 (MLP) 神经网络。数据平衡显著提高了分类的准确性。最佳的整体分类准确性从 95.8%到 97.0%不等，具体取决于使用的过采样算法，并且明显优于不平衡数据和下采样数据的分类准确性（分别为 89.6%和 93.5%）。与不平衡数据或简单下采样数据相比，一些算法允许增加对物体上点的检测 10%。研究结果表明，使用选定的过采样算法可以帮助改善点云分类，使航空激光测深技术更适合海底测绘。
