

FOLLOWUS
1.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China
3.Key Laboratory of Marine Geology and Environment, Center of Deep Sea Research, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
4.Qingdao Institute of Marine Geology, China Geological Survey, Ministry of Natural Resources, Qingdao 266237, China
5.College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China
flyang@sdust.edu.cn
luan@qdio.ac.cn
Received:19 March 2022,
Accepted:15 June 2022,
Online First:29 July 2022,
Published:01 September 2023
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YANG Fanlin,WANG Feng,LUAN Zhendong,et al.Automatic segmentation of gas plumes from multibeam water column images using a U-shape network[J].Journal of Oceanology and Limnology,2023,41(05):1753-1764.
Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids. The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface. A multibeam echo-sounder system (MBES) can record the complete backscatter intensity of the water column
and it is one of the most effective means for detecting cold seeps. However
the gas plumes recorded in multibeam water column images (WCI) are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES
making it difficult to obtain the effective segmentation. Therefore
based on the existing UNet semantic segmentation network
this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes. Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods. The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference. The segmentation precision
the Dice coefficient
and the recall rate of this model are 92.09%
92.00%
and 92.49%
respectively
which are 1.17%
2.10%
and 2.07% higher than the results of the UNet.
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