A new oil spill detection algorithm based on Dempster-Shafer evidence theory

doi:10.1007/s00343-021-0255-2

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与综合管理实验室

	A new oil spill detection algorithm based on Dempster-Shafer evidence theory
	Zhang, Tianlong 1,4; Guo, Jie 1,2,3; Xu, Chenqi 1,4; Zhang, Xi 5; Wang, Chuanyuan 1,2,3; Li, Baoquan 1,2,3
刊名	JOURNAL OF OCEANOLOGY AND LIMNOLOGY
	2021-09-16
页码	14
关键词	synthetic aperture radar (SAR) data oil spill detection subjective Bayesian Faster-region convolutional neural networks (RCNN) Dempster-Shafer evidence theory
ISSN号	2096-5508
DOI	10.1007/s00343-021-0255-2
通讯作者	Guo, Jie(jguo@yic.ac.cn)
英文摘要	Features of oil spills and look-alikes in polarimetric synthetic aperture radar (SAR) images always play an important role in oil spill detection. Many oil spill detection algorithms have been implemented based on these features. Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes, some oil spill detection algorithms do not consider the environmental factors. To distinguish oil spills and look-alikes more accurately based on environmental factors and image features, a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed. The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model. The Faster-region convolutional neural networks (RCNN) model was used for oil spill detection based on the convolution features. The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory. The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1 798 image samples and environmental information records related to the image samples. The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate, with an identification rate greater than 75% and a false alarm rate lower than 19% from experiments. A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm. The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.
资助项目	National Key R&D Program of China[2017YFC1405600] ; National Natural Science Foundation of China[42076197] ; National Natural Science Foundation of China[41576032] ; Major Program for the International Cooperation of the Chinese Academy of Sciences[133337KYSB20160002]
WOS关键词	SAR ; SATELLITE ; RADARSAT ; ENVISAT ; SEA
WOS研究方向	Marine & Freshwater Biology ; Oceanography
语种	英语
WOS记录号	WOS:000696496400003
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Major Program for the International Cooperation of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/29761]
专题	烟台海岸带研究所_海岸带信息集成与综合管理实验室烟台海岸带研究所_海岸带生物学与生物资源利用所重点实验室烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者	Guo, Jie
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res YIC, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Shandong Key Lab Coastal Environm Proc, Yantai 264003, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Minist Nat Resources MNR, Inst Oceanog FIO 1, Qingdao 266061, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Tianlong,Guo, Jie,Xu, Chenqi,et al. A new oil spill detection algorithm based on Dempster-Shafer evidence theory[J]. JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2021:14.
APA	Zhang, Tianlong,Guo, Jie,Xu, Chenqi,Zhang, Xi,Wang, Chuanyuan,&Li, Baoquan.(2021).A new oil spill detection algorithm based on Dempster-Shafer evidence theory.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,14.
MLA	Zhang, Tianlong,et al."A new oil spill detection algorithm based on Dempster-Shafer evidence theory".JOURNAL OF OCEANOLOGY AND LIMNOLOGY (2021):14.