Lai Wei, Zhuowei Hu, Meichen Guo, Minbin Jiang, Shuo Zhang
{"title":"SAR图像溢油监测中的纹理特征分析","authors":"Lai Wei, Zhuowei Hu, Meichen Guo, Minbin Jiang, Shuo Zhang","doi":"10.1109/Geoinformatics.2012.6270284","DOIUrl":null,"url":null,"abstract":"This paper introduces the oil spill monitoring in SAR image by texture analysis and spectral information. In texture analysis, it discusses the parameters of texture extraction, and makes experiment. Then it determines the 17*17 as the windows size, 90 as the angle and 5 as distance. Through neural network classification, these parameters are suitable for oil spill monitoring. It can distinguish with oil and oil-like well. Its accuracy is satisfactory. These parameters are not only used for oil spill monitoring, but also provide a foundation for deeper SAR image classification.","PeriodicalId":259976,"journal":{"name":"2012 20th International Conference on Geoinformatics","volume":"390 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Texture feature analysis in oil spill monitoring by SAR image\",\"authors\":\"Lai Wei, Zhuowei Hu, Meichen Guo, Minbin Jiang, Shuo Zhang\",\"doi\":\"10.1109/Geoinformatics.2012.6270284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces the oil spill monitoring in SAR image by texture analysis and spectral information. In texture analysis, it discusses the parameters of texture extraction, and makes experiment. Then it determines the 17*17 as the windows size, 90 as the angle and 5 as distance. Through neural network classification, these parameters are suitable for oil spill monitoring. It can distinguish with oil and oil-like well. Its accuracy is satisfactory. These parameters are not only used for oil spill monitoring, but also provide a foundation for deeper SAR image classification.\",\"PeriodicalId\":259976,\"journal\":{\"name\":\"2012 20th International Conference on Geoinformatics\",\"volume\":\"390 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 20th International Conference on Geoinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/Geoinformatics.2012.6270284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 20th International Conference on Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Geoinformatics.2012.6270284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Texture feature analysis in oil spill monitoring by SAR image
This paper introduces the oil spill monitoring in SAR image by texture analysis and spectral information. In texture analysis, it discusses the parameters of texture extraction, and makes experiment. Then it determines the 17*17 as the windows size, 90 as the angle and 5 as distance. Through neural network classification, these parameters are suitable for oil spill monitoring. It can distinguish with oil and oil-like well. Its accuracy is satisfactory. These parameters are not only used for oil spill monitoring, but also provide a foundation for deeper SAR image classification.