文献类型：期刊文献

英文题名：Marine Oil Spill Detection with X-Band Shipborne Radar Using GLCM, SVM and FCM

作者：Li, Bo[1];Xu, Jin[1];Pan, Xinxiang[1];Ma, Long[1];Zhao, Zhiqiang[1];Chen, Rong[1];Liu, Qiao[1];Wang, Haixia[2]

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524088, Peoples R China;[2]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China

年份：2022

卷号：14

期号：15

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:000839742600001)、、EI(收录号：20223612687121)、Scopus(收录号：2-s2.0-85137116772)、WOS

基金：This research was funded by the National Natural Science Foundation of China, grant number 52071090; the Natural Science Foundation of Guangdong Province, grant number 2022A1515011603; the Special projects in key fields (Artificial Intelligence) of Universities in Guangdong Province, grant number 2019KZDZX1035; the Research start-up funding project of Guangdong Ocean University, grant number 060302132009.

语种：英文

外文关键词：shipborne radar; oil spill; GLCM; SVM; FCM

外文摘要：Marine oil spills have a significant adverse impact on the economy, ecology, and human health. Rapid and effective oil spill monitoring action is extraordinarily important for controlling marine pollution. A marine oil spill detection scheme based on X-band shipborne radar image with machine learning is proposed here. First, the original shipborne radar image collected on Dalian 7.16 oil spill accident was transformed into a Cartesian coordinate system and noise suppressed. Then, texture features and SVM were used to indicate the effective monitoring location of ocean waves. Third, FCM was applied to classify the oil films and ocean waves. Finally, the oil spill detection result was transformed back to a polar coordinate system. Compared with an improved active contour model and another oil spill detection method with SVM, our method performed more intelligently. It can provide data support for marine oil spill emergency response.