文献类型：期刊文献

英文题名：Marine Radar Oil Spill Extraction Based on Texture Features and BP Neural Network

作者：Chen, Rong[1];Jia, Baozhu[1,2];Ma, Long[1];Xu, Jin[1,3];Li, Bo[1];Wang, Haixia[4]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524091, Peoples R China;[2]Tech Res Ctr Ship Intelligence & Safety Engn Guang, Zhanjiang 524006, Peoples R China;[3]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518116, Peoples R China;[4]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China

年份：2022

卷号：10

期号：12

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000901269500001)、、Scopus(收录号：2-s2.0-85144930805)、WOS

语种：英文

外文关键词：marine radar; oil spill; BP neural network; principal component analysis; texture features; machine learning

外文摘要：Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m(2). The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents.