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Sea surface oil spill identification method based on SAR polarization ratio and texture feature
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Han Chen1, Tao Xie2, 3, *, He Fang1, Lei Meng4, Li Zhao1, Runbing Ai1
Haiyang Xuebao | 2019, 41(9) : 181 - 190
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Haiyang Xuebao | 2019, 41(9): 181-190
• Marine Information Science •
Sea surface oil spill identification method based on SAR polarization ratio and texture feature
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Han Chen1, Tao Xie2, 3, *, He Fang1, Lei Meng4, Li Zhao1, Runbing Ai1
Affiliations
  • 1 School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
  • 3 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 4 Beijing City 5111 Mailbox, Beijing 100094, China
Published: 2019-09-25 doi: 10.3969/j.issn.0253-4193.2019.09.017
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Aiming at the characteristics of SAR images on the ocean surface, the texture feature method based on gray level co-occurrence matrix is a common method for extracting oil spill information from the sea surface, but the complex information on the actual ocean surface makes the SAR image produce a dark spot area similar to the oil spill phenomenon. The false alarm rate is obtained when the oil feature information is extracted by the texture feature method, and the extraction precision of the oil spill information is reduced. Based on the RADARSAT-2 SAR quadratic polarization image, this paper proposes a texture feature recognition method based on SAR polarization ratio image to identify and extract the oil film on the sea surface. The results show that the texture feature recognition method based on SAR polarization ratio image can effectively and accurately extract the oil spill information on the sea surface. Compared with the texture feature recognition method of VV polarization image, the false alarm rate in the oil spill monitoring process is reduced by 17.96 %, the overall accuracy of oil spill monitoring reached 96.83%.

synthetic aperture radar  /  oil spill identification  /  polarization ratio  /  texture feature
Han Chen, Tao Xie, He Fang, Lei Meng, Li Zhao, Runbing Ai. Sea surface oil spill identification method based on SAR polarization ratio and texture feature[J]. Haiyang Xuebao, 2019 , 41 (9) : 181 -190 . DOI: 10.3969/j.issn.0253-4193.2019.09.017
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Article Info
doi: 10.3969/j.issn.0253-4193.2019.09.017
  • Receive Date：2018-09-08
  • Online Date：2026-04-03
  • Published：2019-09-25
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History
  • Received：2018-09-08
  • Revised：2018-11-25
Funding
Affiliations
    1 School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
    3 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
    4 Beijing City 5111 Mailbox, Beijing 100094, China
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光柄菇属 Pluteus 5 2.39
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栓菌属 Trametes 5 2.39
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