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Oil spill discrimination of multi-time-domain shipborne radar images using active contour model
Xu, Jin1; Pan, Xinxiang1; Wu, Xuerui2,3,4; Jia, Baozhu1; Fei, Juan5; Wang, Haixia6; Li, Bo7; Cui, Can8
2021-03-07
Source PublicationGEOSCIENCE LETTERS
ISSN2196-4092
Volume8Issue:1Pages:13
AbstractAccidental oil spills cause serious pollution to the ocean and are difficult to control in short time. It is an important guarantee for emergency disposal to effectively monitor oil spills. Remote sensing is the main means to monitor oil spills. High false alarm rate has been an important bottleneck of this technology. In this paper, a multi-time-domain shipborne radar images discrimination mechanism was proposed. Based on the improved Sobel operator, Otsu and linear interpolation, the co-frequency interference noises were detected and suppressed. Gray intensity correction model (GICM) and dual-threshold method were used to eliminate highlighted continuous pixels. Oil films were extracted by using an active contour model (ACM). Finally, a multi-time-domain discrimination mechanism based on variation range tolerance of identified oil films centroids was designed to reduce the false alarm rate. It can provide technical support for decision-making and emergency response.
KeywordOil spill Shipborne radar Active contour model Multi-time-domain
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University
DOI10.1186/s40562-021-00178-8
Language英语
Funding ProjectNational Natural Science Foundation of China[51709031] ; National Natural Science Foundation of China[51979045] ; Fundamental Research Funds for the Central Universities[3132019138] ; Innovation Support Project of Dalian[2018RQ22] ; Enterprise-university-research Cooperation Project of the Ministry of Education of China[201702043016] ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province[2019KZDZX1035] ; Guangdong Ocean University
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; Innovation Support Project of Dalian ; Innovation Support Project of Dalian ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Enterprise-university-research Cooperation Project of the Ministry of Education of China ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province ; Guangdong Ocean University ; Guangdong Ocean University
WOS Research AreaGeology ; Meteorology & Atmospheric Sciences
WOS SubjectGeosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS IDWOS:000626085300001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/78714
Collection中国科学院国家天文台
Corresponding AuthorWu, Xuerui
Affiliation1.Guangdong Ocean Univ, Maritime Coll, Zhanjiang, Guangdong, Peoples R China
2.Chifeng Univ, Sch Resources Environm & Architectural Engn, Chifeng, Inner Mongolia, Peoples R China
3.Chinese Acad Sci, Shanghai Astron Observ, Shanghai, Peoples R China
4.Shanghai Key Lab Space Nav & Positioning Tech, Shanghai, Peoples R China
5.Lingnan Normal Univ, Informat Engn Coll, Zhanjiang, Guangdong, Peoples R China
6.Dalian Maritime Univ, Nav Coll, Dalian, Liaoning, Peoples R China
7.Guangdong Ocean Univ, Continuing Educ Coll, Zhanjiang, Guangdong, Peoples R China
8.Shenyang Aerosp Univ, Civil Aviat Coll, Shenyang, Liaoning, Peoples R China
Recommended Citation
GB/T 7714
Xu, Jin,Pan, Xinxiang,Wu, Xuerui,et al. Oil spill discrimination of multi-time-domain shipborne radar images using active contour model[J]. GEOSCIENCE LETTERS,2021,8(1):13.
APA Xu, Jin.,Pan, Xinxiang.,Wu, Xuerui.,Jia, Baozhu.,Fei, Juan.,...&Cui, Can.(2021).Oil spill discrimination of multi-time-domain shipborne radar images using active contour model.GEOSCIENCE LETTERS,8(1),13.
MLA Xu, Jin,et al."Oil spill discrimination of multi-time-domain shipborne radar images using active contour model".GEOSCIENCE LETTERS 8.1(2021):13.
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