Zhou, Q.; Li, X.; Liu, L.; Wang, J.; Zhang, L.; Liu, B. Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis. Remote Sens.2023, 15, 3576.
Zhou, Q.; Li, X.; Liu, L.; Wang, J.; Zhang, L.; Liu, B. Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis. Remote Sens. 2023, 15, 3576.
Zhou, Q.; Li, X.; Liu, L.; Wang, J.; Zhang, L.; Liu, B. Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis. Remote Sens.2023, 15, 3576.
Zhou, Q.; Li, X.; Liu, L.; Wang, J.; Zhang, L.; Liu, B. Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis. Remote Sens. 2023, 15, 3576.
Abstract
The determination and classification of seafloor sediment types are crucial for the exploitation of marine resources, construction of marine engineering, and maintenance of marine ecological en-vironments. Automatic classification of seafloor sediment based on acoustic telemetry data is an important method to quickly understand the type of a large range of sediment. Currently, most studies on sediment classification are based on multi-beam backscattering intensity data, which is a relatively single data type. Besides, the low-dimensional details of standard U-Net gradually weaken in the propagation process, limiting the accuracy of sediment classification. Therefore, this study proposes an automatic classification method of seafloor sediment types based on an im-proved U-Net and K-means clustering algorithm, using multi-beam water depth, sub-bottom profile, and sample test data in the Northern Slope of the South China Sea. Six sediment types, including gravelly muddy sand, sand, silty sand, less muddy silt, muddy silt, and silty mud, were identified in the study area. Additionally, the study area was divided into four sedimentary en-vironment zones, including the shelf sedimentary area, the upper shelf slope break sedimentary area, the lower shelf slope break mixed sedimentary area, and the slope deposition area, based on the results of sediment classification and geological background. The sedimentary environment zones were found to be distributed along the trend of the shelf slope break line. The results of this study not only provide an important supplement to the existing classification methods of seafloor sediments but also contribute to the understanding of the sedimentary environment and process of the Northern Slope of the South China Sea.
Keywords
improved U-Net; seabed sediment classification; sub-bottom profiling; multibeam bathymetry; Northern Slope of the South China Sea
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
