Deep learning model for seabed sediment classification based on fuzzy ranking feature optimization

X Cui, F Yang, X Wang, B Ai, Y Luo, D Ma - Marine Geology, 2021 - Elsevier
Accurate acquisition of information on seabed sediment distributions plays an important role
in the construction of basic marine geographic databases. Although a multibeam echo …

[HTML][HTML] MBES seabed sediment classification based on a decision fusion method using deep learning model

J Wan, Z Qin, X Cui, F Yang, M Yasir, B Ma, X Liu - Remote Sensing, 2022 - mdpi.com
High-precision habitat mapping can contribute to the identification and quantification of the
human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment …

DNN-based seabed classification using differently weighted MBES multifeatures

Z Zhu, X Cui, K Zhang, B Ai, B Shi, F Yang - Marine Geology, 2021 - Elsevier
Seabed sediment classification has significance for the utilization of marine resources and
marine scientific research. Currently, the multibeam echo sounder (MBES) is increasingly …

CNN multibeam seabed sediment classification combined with a novel feature optimization method

M Anokye, X Cui, F Yang, M Fan, Y Luo… - Mathematical Geosciences, 2024 - Springer
The classification of seabed sediments is an essential aspect of marine spatial planning and
management. Multibeam echo sounders (MBESs) have been widely used for efficient and …

Optimizing multi-classifier fusion for seabed sediment classification using machine learning

M Anokye, X Cui, F Yang, P Wang, Y Sun… - … Journal of Digital …, 2024 - Taylor & Francis
Seabed sediment mapping with acoustical data and ground-truth samples is a growing field
in marine science. In recent years, multi-classifier ensemble models have gained …

Seabed mixed sediment classification with multi-beam echo sounder backscatter data in Jiaozhou Bay

Q Tang, N Lei, J Li, Y Wu, X Zhou - Marine Georesources & …, 2015 - Taylor & Francis
The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry
data, but also obtain high-resolution seabed backscatter strength data. A number of studies …

Sediment classification of small-size seabed acoustic images using convolutional neural networks

X Luo, X Qin, Z Wu, F Yang, M Wang, J Shang - IEEE Access, 2019 - ieeexplore.ieee.org
Seabed acoustic images are image data mosaics derived from seafloor acoustic
backscattering intensity data, which is related to the type of sediment covering the seabed …

[HTML][HTML] Classification of marine sediment in the northern slope of the South China Sea based on improved U-Net and K-means clustering analysis

Q Zhou, X Li, L Liu, J Wang, L Zhang, B Liu - Remote Sensing, 2023 - mdpi.com
The classification of marine sediment based on acoustic data is crucial for various
applications such as marine resource exploitation, marine engineering construction, and …

Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model

X Ji, B Yang, Q Tang - Applied Acoustics, 2020 - Elsevier
Seabed sediment classification using acoustic remote sensing technique is an attractive
approach due to its high coverage capabilities and limited costs compared to taking samples …

Optimizing the sediment classification of small side-scan sonar images based on deep learning

X Qin, X Luo, Z Wu, J Shang - IEEE Access, 2021 - ieeexplore.ieee.org
Acoustic seabed classification (ASC) is a fast and large-scale seabed sediment survey
method. In particular, combining it with an automated classifier can theoretically achieve fast …