A new oil spill detection algorithm based on Dempster-Shafer evidence theory
文献类型：CNKI期刊论文
| 作者 | Tianlong ZHANG; Jie GUO; Chenqi XU; Xi ZHANG; Chuanyuan WANG; Baoquan LI |
| 发表日期 | 2022-03-15 |
| 出处 | Journal of Oceanology and Limnology
 |
| 关键词 | synthetic aperture radar (SAR) data oil spill detection subjective Bayesian Faster-region convolutional neural networks(RCNN) Dempster-Shafer evidence theory |
| 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%. |
| 文献子类 | CNKI期刊论文 |
| 资助机构 | Supported by the National Key R&D Program of China (No. 2017YFC1405600) ; the National Natural Science Foundation of China (Nos. 42076197, 41576032) ; the Major Program for the International Cooperation of the Chinese Academy of Sciences (No. 133337KYSB20160002) |
| 卷 | v.40期:02页:72-85 |
| 语种 | 英文; |
| 分类号 | X55;U698.7 |
| ISSN号 | 2096-5508 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/187472]  |
| 专题 | 中国科学院海洋研究所 |
| 作者单位 | 1.ShandongKeyLaboratoryofCoastalEnvironmentalProcesses,YantaiInstituteofCoastalZoneResearch,ChineseAcademyofSciences 2.FristInstituteofOceanography(FIO),MinistryofNaturalResources(MNR) 3.YantaiInstituteofCoastalZoneResearch(YIC),ChineseAcademyofSciences(CAS),CASKeyLaboratoryofCoastalEnvironmentalProcessesandEcologicalRemediation 4.UniversityoftheChineseAcademyofSciences 5.CenterforOceanMega-Science,ChineseAcademyofSciences |
| 推荐引用方式 GB/T 7714 | Tianlong ZHANG,Jie GUO,Chenqi XU,et al. A new oil spill detection algorithm based on Dempster-Shafer evidence theory. 2022. |
入库方式： OAI收割
来源：海洋研究所
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