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Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Jin Xu; Xinxiang Pan; Baozhu Jia; Xuerui Wu; Peng Liu; Bo Li
Journal of Marine Science and Engineering · Vol. 9, Issue 1 · pp. 65 · 2021

Abstract

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 Yukun 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.

Bibliographic Information

JournalJournal of Marine Science and Engineering
PublisherMDPI
Publication Date2021-01-10
Publication Year2021
Volume9
Issue1
Pages65
Document TypeJournal Article
eISSN2077-1312
DOI10.3390/jmse9010065
SubjectMarine science; oceanography; marine engineering; coastal science; marine environment

Access Information

NARA Access CoverageOA / free full text
Journal Homepagehttps://www.mdpi.com/journal/jmse
Publisher PageOpen Publisher Page
This article is openly available from the publisher.