Results 1 to 10 of about 29,357 (211)

Automatic Marine Sub-Bottom Sediment Classification Using Feature Clustering and Quality Factor

Journal of Marine Science and Engineering, 2023
It has been proven that the quality factor (Q) is important for marine sediment attenuation attribute representation and is helpful for sediment classification.
Zaixiang Zong, Jianhu Zhao, Shaobo Li, Hongmei Zhang   +3 more
doaj   +3 more sources

Sub-Bottom Sediment Classification Using Reliable Instantaneous Frequency Calculation and Relaxation Time Estimation

Remote Sensing, 2021
The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site ...
Shaobo Li, Jianhu Zhao, Hongmei Zhang, Siheng Qu   +3 more
doaj   +3 more sources

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   +3 more sources

A Novel Acoustic Sediment Classification Method Based on the K-Mdoids Algorithm Using Multibeam Echosounder Backscatter Intensity

Journal of Marine Science and Engineering, 2021
The modern discrimination of sediment is based on acoustic intensity (backscatter) information from high-resolution multibeam echo-sounder systems (MBES).
Xiaochen Yu, Jingsheng Zhai, Bo Zou, Qi Shao, Guangchao Hou   +4 more
doaj   +3 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   +3 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   +3 more sources

Seafloor Sediment Classification Using Small-Sample Multi-Beam Data Based on Convolutional Neural Networks

Journal of Marine Science and Engineering
Accurate, rapid, and automatic seafloor sediment classification represents a crucial challenge in marine sediment research. To address this, our study proposes a seafloor sediment classification method integrating convolutional neural networks (CNNs ...
Haibo Ma, Xianghua Lai, Taojun Hu, Xiaoming Fu, Xingwei Zhang, Sheng Song   +5 more
doaj   +3 more sources

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

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   +3 more sources

Water motion characteristics and water damping correction in gap between ship-to-ship system

Zhongguo Jianchuan Yanjiu, 2023
ObjectivesThis study seeks to correct the distortion of results caused by the inviscid-flow assumption when potential flow theory is used to calculate a two-ship floating system with a small gap, and analyze the motion response characteristics of the gap
Liqin LIU, Chunlei MENG, Wenjun SHEN, Yilun LIU, Ruoyu ZHANG   +4 more
doaj   +1 more source

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