Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image
Oil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this pa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/1/65 |
| _version_ | 1797413598254858240 |
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| author | Jin Xu Xinxiang Pan Baozhu Jia Xuerui Wu Peng Liu Bo Li |
| author_facet | Jin Xu Xinxiang Pan Baozhu Jia Xuerui Wu Peng Liu Bo Li |
| author_sort | Jin Xu |
| collection | DOAJ |
| description | Oil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this paper, the original shipborne radar image collected by the teaching-practice ship <i>Yukun</i> of Dalian Maritime University during the oil spill accident of Dalian on 16 July 2010 was taken as the research data, and an oil spill detection method was proposed by using LBP texture feature and K-means algorithm. First, Laplacian operator, Otsu algorithm, and mean filter were used to suppress the co-frequency interference noises and high brightness pixels. Then the gray intensity correction matrix was used to reduce image nonuniformity. Next, using LBP texture feature and K-means clustering algorithm, the effective oil spill regions were extracted. Finally, the adaptive threshold was applied to identify the oil films. This method can automatically detect oil spills in shipborne radar image. It can provide a guarantee for real-time monitoring of oil spill accidents. |
| first_indexed | 2024-03-09T05:20:18Z |
| format | Article |
| id | doaj.art-931f82dda4a6437e80529908aa9d0df0 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T05:20:18Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-931f82dda4a6437e80529908aa9d0df02023-12-03T12:41:24ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-01-01916510.3390/jmse9010065Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar ImageJin Xu0Xinxiang Pan1Baozhu Jia2Xuerui Wu3Peng Liu4Bo Li5Maritime College, Guangdong Ocean University, Zhanjiang 524088, ChinaMaritime College, Guangdong Ocean University, Zhanjiang 524088, ChinaMaritime College, Guangdong Ocean University, Zhanjiang 524088, ChinaSchool of Resources, Environment and Architectural Engineering, Chifeng University, Chifeng 024000, Inner Mongolia, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaContinuing Education College, Guangdong Ocean University, Zhanjiang 524088, ChinaOil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this paper, the original shipborne radar image collected by the teaching-practice ship <i>Yukun</i> of Dalian Maritime University during the oil spill accident of Dalian on 16 July 2010 was taken as the research data, and an oil spill detection method was proposed by using LBP texture feature and K-means algorithm. First, Laplacian operator, Otsu algorithm, and mean filter were used to suppress the co-frequency interference noises and high brightness pixels. Then the gray intensity correction matrix was used to reduce image nonuniformity. Next, using LBP texture feature and K-means clustering algorithm, the effective oil spill regions were extracted. Finally, the adaptive threshold was applied to identify the oil films. This method can automatically detect oil spills in shipborne radar image. It can provide a guarantee for real-time monitoring of oil spill accidents.https://www.mdpi.com/2077-1312/9/1/65oil spillLBPK-meansshipborne radarremote sensingoil pollution |
| spellingShingle | Jin Xu Xinxiang Pan Baozhu Jia Xuerui Wu Peng Liu Bo Li Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image Journal of Marine Science and Engineering oil spill LBP K-means shipborne radar remote sensing oil pollution |
| title | Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image |
| title_full | Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image |
| title_fullStr | Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image |
| title_full_unstemmed | Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image |
| title_short | Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image |
| title_sort | oil spill detection using lbp feature and k means clustering in shipborne radar image |
| topic | oil spill LBP K-means shipborne radar remote sensing oil pollution |
| url | https://www.mdpi.com/2077-1312/9/1/65 |
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