Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data - Libin Du; Dawei Wan; Xiangqian Meng; Wenjing Li; Guangxin Liang; Hongyu Li (Journal of Marine Science and Engineering)
The classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high accuracy and mobility. However, accurately classifying shallow marine sediments remains a challenging endeavor due to the difficulties associated with differentiation and the inherent limitations in accuracy. To achieve the accurate classification of underwater sediments, a feature selection method for underwater sediment classification is proposed in this paper and tested in a laboratory environment. The method inputs the original feature set into a classification algorithm that combines Sequential Forward Selection with Random Forests. The study demonstrates that the model achieves an overall classification accuracy of 94.1% and a Kappa coefficient of 91.11%, thereby enabling the accurate and efficient classification of underwater sediment. This approach can be employed as a supplementary technique for the precise classification of shallow marine sediments, offering valuable assistance in the examination of marine ecosystems.