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Seabed Sediment Classification Using Spatial Statistical Characteristics

Journal of Marine Science and Engineering, 2022
Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered.
Quanyin Zhang, Jianhu Zhao, Shaobo Li, Hongmei Zhang   +3 more
doaj   +2 more sources

Small-Sample Seabed Sediment Classification Based on Deep Learning

Remote Sensing, 2023
Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier.
Yuxin Zhao, Kexin Zhu, Ting Zhao, Liangfeng Zheng, Xiong Deng   +4 more
doaj   +2 more sources

Sediment Classification of Small-Size Seabed Acoustic Images Using Convolutional Neural Networks [PDF]

IEEE Access, 2019
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.
Xiaowen Luo, Xiaoming Qin, Ziyin Wu, Fanlin Yang, Mingwei Wang, Jihong Shang   +5 more
doaj   +2 more sources

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

International Journal of Digital Earth, 2023
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 prominence for classification problems by combining several base classifiers ...
Michael Anokye, Xiaodong Cui, Fanlin Yang, Ping Wang, Yuewen Sun, Hadong Ma, Emmanuel Oduro Amoako   +6 more
doaj   +2 more sources

Research on Seabed Sediment Classification Based on the MSC-Transformer and Sub-Bottom Profiler

Journal of Marine Science and Engineering, 2023
This paper proposed an MSC-Transformer model based on the Transformer’s neural network, which was applied to seabed sediment classification. The data came from about 2900 km2 of seabed area on the northern slope of the South China Sea.
Han Wang, Qingjie Zhou, Shuo Wei, Xiangyang Xue, Xinghua Zhou, Xiaobo Zhang   +5 more
doaj   +2 more sources

MBES Seabed Sediment Classification Based on a Decision Fusion Method Using Deep Learning Model

Remote Sensing, 2022
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 classification is crucial for marine geological research, marine ...
Jiaxin Wan, Zhiliang Qin, Xiaodong Cui, Fanlin Yang, Muhammad Yasir, Benjun Ma, Xueqin Liu   +6 more
doaj   +2 more sources

Classification of Seabed Sediment by Combining Airborne LiDAR Bathymetry and Multispectral Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The classification of seabed sediment plays an important role in marine ecological environment protection and other related fields. To fully explore the application ability of marine geographic information in seabed sediment classification, this article ...
Dianpeng Su, Han Gao, Anxiu Yang, Juan Wang, Xiaozheng Mai, Xudong Liu, Fanlin Yang, Ziyin Wu   +7 more
doaj   +2 more sources

Multifrequency backscatter classification of seabed sediments using MBES: an integrated approach with ground-truth validation

Frontiers in Marine Science
Accurate seabed sediment classification is essential for mapping marine geological features, assessing benthic habitat, and planning coastal infrastructure.
Moonsoo Lim, Jeongwon Kang, Sunhee Hwang, Eunho Jung, Byung-Cheol Kum, Jongsin Kim   +5 more
doaj   +2 more sources

Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

IEEE Access, 2021
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 automatic seabed sediment classification.
Xiaoming Qin, Xiaowen Luo, Ziyin Wu, Jihong Shang   +3 more
doaj   +1 more source

Seabed Sediment Classification using Multifrequency Backscatter

IOP Conference Series: Earth and Environmental Science, 2023
Abstract Seabed sediment serves as an important metric for assessing marine ecosystems. Different sectors, including the development and utilization of marine resources, marine engineering, environmental research, marine fisheries, and maritime navigation safety, all depend on the classification and identification of different forms of ...
Muhammad Aldila Syariz, Miftakhul Fauzy
openaire   +1 more source