乳化过程中油膜厚度及 油种识别微波探测技术实验展望

Synthetic aperture radar (SAR) has been playing a major role in the monitoring of oil spill, which can image ocean surfaces during the day and night with high resolution and large coverage, independent of cloud cover. However, SAR also has short comings in monitoring oil spillage and oil species identification. It was found thatwater content can describe the oil emulsification process well, a certain amount of emulsified oil can increase the normalized radar cross section (NRCS), and the oil film thickness has 14 海 岸 科 学 2021 年 a linear relationship with the NRCS during the oil emulsification experiment on the basis of the previous National Natural Science Foun dation project (NNSFP). This project is based on the research of the previous NNSFP, in physical oceanography, the electromagnetic scattering theory of the surface and working principle of the hyperspectral imager as the foundation, inversion of the oil film thickness with the hyperspectral data observation by unmanned aerial vehicle through the experiment and joint SAR synchronous observation of e mulsified oil film, to establish the correspondences to the emulsified oil film thickness with NRCS, damping ratio, respectively. Emul sified oil, crude oil and biological oil films were identified by the characteristic parameters of oil spill and emulsification scattering and a microwave detection model of oil film thickness during emulsification was established. The research of this project will provide the possibility for SAR to monitor the amount of emulsified oil spillage ( oil spill thickness) on the sea surface and identify oil species, which will further improve the accuracy and ability of SAR to monitor oil spill on the sea surface

基于微波散射实验的油种识别研究

海上溢油来源复杂,溢油种类多样,正确识别溢油类型对于溢油应急的快速反应具有重要意义。合成孔径雷达(synthetic aperture radar,SAR)具有全天时全天候的监测优势,在海面溢油监测中发挥着主力军作用,但在油种识别方面存在不足。利用C波段全极化散射计对柴油、原油、油水混合物和棕榈油进行外场实验观测,探究微波识别油膜的敏感特征参数,并将敏感特征参数应用于海上油膜实验获取的SAR图像进行油种识别。结果表明,在垂直(vertical transmission vertical reception,VV)极化方式下的油水差(Δσ~0)可以有效识别植物油和矿物油;基于dB和linear units表达的后向散射系数(N_(RCS))计算的抑制比(D_R)在垂直(VV)和水平(horizontal transmission horizontal reception,HH)极化方式下可以有效识别植物油和矿物油,并且在交叉(vertical transmission horizontal reception/horizontal transmission vertical reception,VH/HV)极化方式下linear units表达的N_(RCS)计算的抑制比可以识别原油和乳化油;极化差(PD)可用于识别原油、乳化油和植物油

基于微波散射实验的油种识别研究

海上溢油来源复杂,溢油种类多样,正确识别溢油类型对于溢油应急的快速反应具有重要意义。合成孔径雷达(synthetic aperture radar,SAR)具有全天时全天候的监测优势,在海面溢油监测中发挥着主力军作用,但在油种识别方面存在不足。利用C波段全极化散射计对柴油、原油、油水混合物和棕榈油进行外场实验观测,探究微波识别油膜的敏感特征参数,并将敏感特征参数应用于海上油膜实验获取的SAR图像进行油种识别。结果表明,在垂直(vertical transmission vertical reception,VV)极化方式下的油水差(Δσ~0)可以有效识别植物油和矿物油;基于dB和linear units表达的后向散射系数(N_(RCS))计算的抑制比(D_R)在垂直(VV)和水平(horizontal transmission horizontal reception,HH)极化方式下可以有效识别植物油和矿物油,并且在交叉(vertical transmission horizontal reception/horizontal transmission vertical reception,VH/HV)极化方式下linear units表达的N_(RCS)计算的抑制比可以识别原油和乳化油;极化差(PD)可用于识别原油、乳化油和植物油

基于微波散射实验的油种识别研究

海上溢油来源复杂,溢油种类多样,正确识别溢油类型对于溢油应急的快速反应具有重要意义。合成孔径雷达(synthetic aperture radar,SAR)具有全天时全天候的监测优势,在海面溢油监测中发挥着主力军作用,但在油种识别方面存在不足。利用C波段全极化散射计对柴油、原油、油水混合物和棕榈油进行外场实验观测,探究微波识别油膜的敏感特征参数,并将敏感特征参数应用于海上油膜实验获取的SAR图像进行油种识别。结果表明,在垂直(vertical transmission vertical reception,VV)极化方式下的油水差(Δσ~0)可以有效识别植物油和矿物油;基于dB和linear units表达的后向散射系数(N_(RCS))计算的抑制比(D_R)在垂直(VV)和水平(horizontal transmission horizontal reception,HH)极化方式下可以有效识别植物油和矿物油,并且在交叉(vertical transmission horizontal reception/horizontal transmission vertical reception,VH/HV)极化方式下linear units表达的N_(RCS)计算的抑制比可以识别原油和乳化油;极化差(PD)可用于识别原油、乳化油和植物油

Oil spill detection method for SAR images based on the improved Faster R-CNN model

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%

Status and Trends of Remote Sensing Study to Monitor Sea Surface Oil Spill and Enteromorpha

海面溢油和浒苔灾害已经成为当今主要的海洋生态环境问题,而基于卫星遥感影像提取海面溢油和浒苔信息是监测其动态变化的一种有效手段,因此本文对国内外海面溢油及浒苔遥感监测技术进行归纳整理。光学遥感数据多波段比值法是最常用的海面溢油监测方法。另外,合成孔径雷达(SAR)不受雨云影响,在灾害监测中发挥着越来越重要的作用,而利用灰度值或后向散射系数变化来判断溢油或浒苔是SAR常用的方法。从现有的研究可以看出:遥感监测海上溢油及浒苔范围发展最为成熟,已经业务化运行;然而,遥感监测溢油量、溢油类型及浒苔生物量仍然处于试验阶段。遥感海洋灾害的监测要由定性走向定量,真正实现实时、连续、快速、准确,仍需要多种平台和..

Remote sensing information extraction of the Laizhou Bay environment pollution

采用Landsat图像跨度40多年数据,利用遥感手段对莱州湾7大排污口环境污染状况进行了遥感信息提取试验,结果显示与多年来分析调查的结果一致,即通过入海排放口水色变化以及像素分析发现自20世纪90年代以来,莱州湾海水污染逐年加重; 通过MODI数据和ENVISAT-ASAR数据分析提取了莱州湾2011年的海温变化、叶绿素浓度分布和海面溢油信息,结果显示该区域近岸海温和叶绿素浓度偏高,海上溢油风险加大。研究结果表明,利用遥感技术能有效地提取海区环境污染信息,可以作为监控海水污染的有效手段之一。 &nbsp

基于二维激光观测的溢油及其乳化过程散射模式研究进展

合成孔径雷达(SAR)以其高分辨率、能不受雨云影响实施全天时全天候全方位监测,在海面溢油灾害应急监测过程中发挥着越来越重要的作用。溢油是因为海面油膜抑制了毛细波和重力波,在SAR图像上呈暗斑而被识别。然而,海面溢油的乳化过程直接影响SAR对海面溢油后向散射截面的观测精度。本研究以物理海洋学和激光原理以及海面电磁散射理论为基础,通过实验利用激光扫描仪观测海面溢油粗糙度,分别与溢油特征参数、后向散射系数建立对应关系;耦合海面溢油参数与后向散射截面的关系,利用电磁散射数值建模方法,建立海面溢油散射模型,研究海面溢油乳化过程对微波后向散射截面的影响。本项目的研究将为SAR监测海面溢油量、溢油厚度及油品..