Automatic segmentation of gas plumes from multibeam water column images using a U-shape network

doi:10.1007/s00343-022-2139-5

IOCAS-IR > 海洋地质与环境重点实验室

	Automatic segmentation of gas plumes from multibeam water column images using a U-shape network
	Yang, Fanlin 1,2; Wang, Feng1 ; Luan, Zhendong 3; Bu, Xianhai 1,2; Mei, Sai 4; Zhang, Jianxing 3; Liu, Hongxia 5
	2023-06-10
发表期刊	JOURNAL OF OCEANOLOGY AND LIMNOLOGY
ISSN	2096-5508
页码	12
通讯作者	Yang, Fanlin(flyang@sdust.edu.cn) ; Luan, Zhendong(luan@qdio.ac.cn)
摘要	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.
关键词	multibeam water column image (WCI) gas plumes UNet automatic segmentation
DOI	10.1007/s00343-022-2139-5
收录类别	SCI
语种	英语
WOS研究方向	Marine & Freshwater Biology ; Oceanography
WOS类目	Limnology ; Oceanography
WOS记录号	WOS:001004056800004
出版者	SCIENCE PRESS
WOS关键词	METHANE FLUXES ; COLD SEEPS
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/182315
专题	海洋地质与环境重点实验室
通讯作者	Yang, Fanlin; Luan, Zhendong
作者单位	1.Shandong Univ Sci & Technol, Coll Geodesy & Geomatics, Qingdao 266590, Peoples R China 2.Minist Nat Resources, Key Lab Ocean Geomatics, Qingdao 266590, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Ctr Deep Sea Res, Key Lab Marine Geol & Environm, Qingdao 266071, Peoples R China 4.Minist Nat Resources, Qingdao Inst Marine Geol, China Geol Survey, Qingdao 266237, Peoples R China 5.Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China
通讯作者单位	深海极端环境与生命过程研究中心
推荐引用方式 GB/T 7714	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,2023:12.
APA	Yang, Fanlin.,Wang, Feng.,Luan, Zhendong.,Bu, Xianhai.,Mei, Sai.,...&Liu, Hongxia.(2023).Automatic segmentation of gas plumes from multibeam water column images using a U-shape network.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,12.
MLA	Yang, Fanlin,et al."Automatic segmentation of gas plumes from multibeam water column images using a U-shape network".JOURNAL OF OCEANOLOGY AND LIMNOLOGY (2023):12.