基于改进Faster R-CNN模型的SAR图像溢油检测方法

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室

	基于改进Faster R-CNN模型的SAR图像溢油检测方法
	张天龙 1,2,3; 过杰 1,2,4
刊名	海洋科学
	2021
卷号	45 期号:5 页码:103-112
关键词	溢油检测 BP神经网络
ISSN号	1000-3096
其他题名	Oil spill detection method for SAR images based on the improved Faster R-CNN model
文献子类	Article
英文摘要	Oil spill emergency work needs to detect oil spills accurately in synthetic aperture radar (SAR) images.To reduce the influence of human factors on oil spill detection accuracy in the SAR image feature extraction and selection processes,the Faster R-CNN model is introduced and improved in this study.Because of the various shapes of oil spills and the complex background,the VGG16 convolutional network with consistent structure and strong practicability is selected to obtain the image features.The Soft-NMS algorithm is used to optimize the Faster R-CNN model.On the basis of the same dataset,the most frequently used geometric,gray,and texture features of SAR images were extracted to build the backpropagation (BP) artificial neural network oil spill detection model,which is compared with the method proposed in this study.The experimental results show that the detection rate of the improved Faster R-CNN model is 0.78,and the false alarm rate is lower than 0.25.Compared with the BP artificial neural network method,the identification and detection rates of the improved Faster R-CNN model are increased by 4% and 5%,respectively,and the oil spill false alarm rate is decreased by 5%.
语种	中文
CSCD记录号	CSCD:7010342
资助机构	国家重点研发计划项目 ; 国家自然科学基金 ; 国家自然科学基金
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/30265]
专题	烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
作者单位	1.中国科学院烟台海岸带研究所中国科学院环境过程与生态修复重点实验室,山东烟台264003; 2.中国科学院烟台海岸带研究所山东省海岸带环境过程重点实验室,山东烟台264003; 3.中国科学院大学,北京100049; 4.中国科学院海洋大科学中心,山东青岛266071
推荐引用方式 GB/T 7714	张天龙,过杰. 基于改进Faster R-CNN模型的SAR图像溢油检测方法[J]. 海洋科学,2021,45(5):103-112.
APA	张天龙,&过杰.(2021).基于改进Faster R-CNN模型的SAR图像溢油检测方法.海洋科学,45(5),103-112.
MLA	张天龙,et al."基于改进Faster R-CNN模型的SAR图像溢油检测方法".海洋科学 45.5(2021):103-112.