Seabed substrate mapping based on machine learning using MBES data

For mapping of seabed substrate, it is essential to extract and analyze seabed substrate characteristics from measured data such as bathymetry and backscatter.The number of studies have been conducted on seabed substrate classification based on backscatter data obtained from side-scan-sonar, single-beam-echo-sounder, multi-beam-echo-sounder (MBES).These studies presented limitation of low accuracy by mapping of seabed substrate through statistical methods or principal components analysis.The purpose of this study is mapping of seabed substrate using machine learning methods like random forest (RF), support vector machine, and k-nearest neighbor and MBES-based variables.MBES-based variables were built total 23 variables from bathymetry, backscatter, and backscatter-based GLCM statistical methods.Seabed substrate classification schemes were defined 4 classification scheme according to the ratio of gravel, mud, and sand.In the case of RF model and case 4 classification scheme, the F1 score showed the highest accuracy at 82.0%.Through this study, machine learning and MBES-based variables were used to confirm the possibility of high-accuracy mapping of seabed substrate, and it is believed that it can be sued to mapping of seabed substrate in other regions.