一种基于Envisat ASAR波模式数据的有效波高反演算法

The main objective of this paper is to propose a newly developed ocean Significant Wave Height(SWH) retrieval method from Envisat Advanced Synthetic Aperture Radar(ASAR) imagery. A series of wave mode imagery from January, April and May of 2011 are collocated with ERA-Interim reanalysis SWH data. Based on the matched datasets, a simplified empirical relationship between 22 types of SAR imagery parameters and SWH products is developed with the Genetic Algorithms Partial Least-Squares(GA-PLS) model. Two major features of the backscattering coefficient σ_0 and the frequency parameter S_(10) are chosen as the optimal training feature subset of SWH retrieval by using cross validation. In addition, we also present a comparison of the retrieval results of the simplified empirical relationship with the collocated ERA-Interim data. The results show that the assessment index of the correlation coefficient, the bias, the root-mean-square error of cross validation(RMSECV) and the scattering index(SI) are 0.78, 0.07 m, 0.76 m and 0.5, respectively. In addition, the comparison of the retrieved SWH data between our simplifying model and the Jason-2 radar altimeter data is proposed in our study.Moreover, we also make a comparison of the retrieval of SWH data between our developed model and the wellknown CWAVE_ENV model. The results show that satisfying retrieval results are acquired in the low-moderate sea state, but major bias appears in the high sea state, especially for SWH5 m.     

浅海环境下溢油海面的仿真

在考虑水深因素的条件下,提出了一种适合于浅海环境下溢油海面的仿真方法,利用TMA谱模型和Marangoni溢油理论模型,计算有限水深下溢油海面的海浪谱;然后根据海浪的色散关系和Longuet-Higgins海浪模型,计算有限水深下溢油海面的铅直位移;并分析了Kitaigorodskii深度函数的特性。仿真结果表明,有限水深下海面的铅直位移比深水的小,而有限水深下溢油海面的粗糙度要比有限水深下和深水清洁海面的都小,结果与Marangoni溢油理论模型相吻合。     

应用极化合成孔径雷达检测海上溢油研究进展

海上溢油给海洋生态环境带来严重的影响,快速准确地探测溢油对于防灾减灾具有重要的意义。利用卫星遥感探测溢油已成为目前主要的检测手段,大多采用合成孔径雷达(SAR)数据,运用图像处理的方法,开展了多种溢油提取算法的研究,取得了较好的结果,但由于海洋的类溢油现象存在,造成提取信息的精度达不到要求。近年来,国内外运用极化SAR数据开展溢油信息提取研究,从极化分解与相位差等角度对溢油特性分析,能有效地区分一些类溢油现象,得到了较理想的结果。分析了应用SAR数据开展溢油信息提取的研究状况,总结了溢油极化SAR探测的研究,指出了目前研究中存在的不足,并提出了今后溢油极化SAR遥感监测的方向。     

灰色关联分析鉴别海面溢油

建立了灰色关联度分析的海面溢油鉴别模式,给出了常见的14种油鉴别的灰色关联度分析值,提出了油种受风化和实验误差影响而造成其关联度不可辨识的模糊区的概念.任丘原油和0#轻柴油实验误差和风化的关联度模糊区分别为1≥r_0i≥0.9805和1≥r_0i≥0.9149,30#重柴油风化的关联度模糊区为1≥r_0i≥0.9647,大庆原油和20#重柴油实验误差的关联度模糊区分别为1≥r_0i≥0.9359和1≥r_0i≥0.9363.使用油种的关联度模糊区域值和关联度的择近原则,可以鉴别常见的14种油.海面溢油案例的鉴别结果表明,本方法同Euclid贴近度聚类分析一样,较指纹谱图辨识法的准确度高.     

海面溢油无人机高光谱遥感检测与厚度估算方法

海上溢油是海洋国家所面临的共同问题,但至今仍没有一种可靠实用的海上溢油准确识别和油量遥感监测方法。为此,本文以无人机高光谱遥感为手段,开展了海面溢油检测与厚度估算方法研究。实验中,通过搭建室外大型水槽溢油实验装置,获取了模拟真实海洋环境条件下不同溢油量的遥感和现场光谱数据,在此基础上,分析并提取了海上溢油特征光谱波段,给出了海上溢油高光谱检测模型;针对现场实验条件下水面油膜厚度难以测定的问题,设计了3种利用总体溢油量的油膜厚度估算模型。得到如下主要结论:（1）675 nm和699 nm是海上溢油检测的有效特征波段,但对极薄的油膜没有检测能力;（2）提出了归一化溢油指数模型、反比例模型和吸收基线模型等3种海上溢油油膜厚度估算模型,其中对于薄油膜（厚度≤ 5 μm）和厚油膜（厚度>50 μm）,反比例模型是溢油厚度反演的首选也是唯一选择。对于中厚度油膜,晴朗天气条件下,归一化溢油指数模型是油膜厚度反演的首选,同时反比例模型和溢油吸收基线模型也都有较好的反演能力,而在多云天气条件下,反比例模型效果最佳。     

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、溢油厚度及油品分布格局提供了可能;将进一步揭示海面溢油的散射机制,提高SAR海面监测溢油的精度和能力。     

基于SAR的海上溢油监测最佳探测参数分析

基于SAR数据,在后向散射系数计算的基础上,从波段、极化方式、入射角等方面开展了海上溢油监测参数分析,结果表明:基于SAR的海上溢油监测,从波段分析,X波段与C波段较L波段更适合监测海上溢油;从极化方式分析,VV极化较HH,HV极化更适合监测海上溢油;从入射角分析,在计算溢油与海水临界值时,经多次实验,位于远入射角的疑...     

荧光光谱向量空间距离模式识别法鉴别海面溢油源

采用向量空间距离模式识别法鉴别海面溢油源,研究出4个激发波长和4个发射波长用作数据处理的信息点.对未风化的和风化过的原油,燃料油和润滑油数据进行了研究.并采集了十多条船上的油品验证了方法的可靠性.给出了实验条件下的最小向量空间距离,建立了荧光光谱鉴别海面溢油源的新技术.     

A study of oil spill detection using ASAR images

The oil spilled worldwide causes ecological disasters that result in enormous damages to the quality of marine environment, and great expenses on clear-up operations are needed. Due to its wide coverage and day-night all-weather observation capability, Synthetic Aperture Radar (SAR) is an important tool for oil spill monitoring and detection. C-band SAR is well adapted to detect oil pollution because oil slicks dampen the Bragg waves and reduce radar backscattering coefficients. In order to detect the area of oil slicks, the algorithm consists of these steps:Preprocessing, Masking of land areas, Detection of dark spots, Spot feature extraction, Dark spot classification. In this paper, the authors examined two coastal regions around Hong Kong and Yantai, China. The obtained results performed on Envisat ASAR images have demonstrated that it is efficient to detect oil spill around the coastal regions. The methodology still needs to be refined with the collection of more SAR data in the near future.     

多极化SAR溢油探测影响因素分析研究

Polarimetric synthetic aperture radar(SAR) oil spill detection parameters conformity coefficient(μ), Muller matrix parameters(|C|, B_0), the eigenvalues of simplified coherency matrix(λ_(nos)) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena(oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches(low wind and biogenic slick) with the signal below the noise,polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes(low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration.     

Envisat ASAR海浪谱资料的最优插值同化试验

基于第三代海浪数值模式LAGFD-WAM,分别利用4种不同形式的各向同性背景误差相关函数进行了Envisat高级合成孔径雷达(ASAR)海浪谱资料的最优插值同化试验.与4个浮标实测数据的比较验证表明,ASAR海浪谱资料的最优插值能够有效地改进海浪模式有效波高的模拟.4种不同形式的各向同性背景误差相关函数的同化结果相差不大,决定同化效果好坏的关键是对相关距离尺度的选取.针对自回归形式的背景误差相关函数的试验结果表明,相关距离尺度的量级在400-500km时同化效果最好,此时同化后的模式有效波高均方根误差比未同化时相对减小了26%.     

海上溢油极化特征及其探测研究

The SAR(Synthetic Aperture Radar) has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images.Therefore, using SAR data to detect oil spills is becoming progressively popular in operational monitoring, which is useful for oceanic environmental protection and hazard reduction. Research has been conducted on the polarization decomposition and scattering characteristics of oil spills from a scattering matrix using allpolarization of the SAR data, calculation of the polarization parameters, and utilization of the CPD(Co-polarized Phase Difference) of the oil and the sea, in order to extract the oil spill information. This method proves to be effective by combining polarization parameters with the characteristics of oil spill. The results show that when using Bragg, the oil spill backscattering machine with Enopy and a mean scatter α parameter. The oil spill can be successfully identified. However, the parameter mechanism of the oil spill remains unclear. The use of CPD can easily extract oil spill information from the ocean, and the polarization research provides a base for oil spill remote sensing detection.     

Anew oil spill detection algorithm based on Dempster-Shafer evidence theory

Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR) images always play an important role in oil spill detection. Many oil spill detection algorithms have been implemented based on these features. Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes, some oil spill detection algorithms do not consider the environmental factors. To distinguish oil spills and look-alikes more accurately based on environmental fact...     

基于合成孔径雷达影像的海洋溢油纹理特征参数分析

溢油已是当前海洋生态环境破坏的主要因素之一,因此对海洋溢油的检测分析是当前海洋环境保护的一个重要课题。传统的溢油提取仅仅是单独依靠光学影像的光谱信息或者合成孔径雷达(SAR)影像的后向散射系数信息进行提取,这会造成很多同谱异物或者粗糙度相近似的地物错分,因此除了利用传统的影像信息以外,还需结合影像的纹理信息,从而提高溢油提取的精度,减少错分地物的数量。选用2006年渤海地区的三景同轨SAR影像作为数据基础,应用基于灰度共生矩阵的方法对其进行纹理分析。该方法可以很好地对图像区域和表面进行感知并能够从像元的灰度相关性上对纹理特征进行详细描述,因此适合于SAR影像的海洋溢油检测。在纹理分析的过程中有很多的参数需要选择,参数选择的好坏将直接影响最终提取结果的精度。通过对纹理分析过程中的参数进行讨论、实验、选择与验证,最终确定了基于灰度共生矩阵纹理分析中各参数的值,并选择了局部平稳、非相似性、对比度、变化量4个特征量作为溢油提取的纹理特征统计量。将纹理特征与SAR自身的后向散射系数相结合,通过神经网络分类法对其进行分类,并计算出分类精度为80.65%,分类效果良好。由此说明了将影像的传统信息与纹理信息相结合进行溢油提取是一种可行而有效的方法,同时也为后续的海洋溢油检测工作奠定了一定的基础。     

溢油模型与SSD曲线法评估渤海溢油事件的海洋生态风险

海上溢油事故不仅会造成大面积的海水污染,还会对海洋生态系统造成严重破坏。为此,采用有效的方法评估溢油事件引起的生态风险,对防灾减灾工作具有重要意义。本文以发生于2011年6月的渤海蓬莱19-3溢油事故为例,使用两种溢油模型(GNOME轨迹模型与ADIOS风化模型)模拟了事故初期油膜的运动轨迹与风化过程。基于模拟结果,利用CAFE模型(Chemical Aquatic Fate and Effect)拟合了相应的物种敏感度分布(species sensitivity distribution,SSD)曲线,首次结合三种模型工具对渤海进行了生态风险评估研究。结果显示,随着原油的持续泄漏,其主要有毒物质(苯系物)浓度达到了1 300μg/L,超过了1%危害浓度值(Hazard Concentration 1%,HC1)。结果表明,在事故初期所产生的生态风险不可忽视,并且风险(多个物种的潜在影响分数)会在96 h内以每日约1%的趋势增长。本文结合溢油运动轨迹和SSD曲线,绘制出了事故期间的生态风险时空分布图。经过定量化的评估,首次发现事故的整体生态风险随时间呈近似二次函数增长,同溢油轨迹一样,向西北方向扩散,越靠近溢油源的海域生态风险概率越高。     

Interfacial properties of oil spill dispersants and their effects on dispersion effectiveness

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