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遥感技术与应用  2020, Vol. 35 Issue (4): 934-942    DOI: 10.11873/j.issn.1004-0323.2020.4.0934
遥感应用     
基于新极化特征参数的SAR海洋溢油检测
任慧敏1,2(),宋冬梅1,3(),王斌1,3,甄宗晋1,2,刘斌4,张婷5
1.中国石油大学(华东)海洋与空间信息学院,山东 青岛 266580
2.中国石油大学(华东)研究生院,山东 青岛 266580
3.海洋矿物资源实验室 青岛海洋科学技术国家实验室,山东 青岛 266071
4.青岛海洋科学技术国家实验室,山东 青岛 266071
5.国家海洋局第一海洋研究所,山东 青岛 266061
New Polarimetric Feature Parameter for Marine Oil Spill Detection in SAR Images
Huimin Ren1,2(),Dongmei Song1,3(),Bin Wang1,3,Zongjin Zhen1,2,Bin Liu4,Ting Zhang5
1.School of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
2.College of Graduated, China University of Petroleum, Qingdao 266580, China
3.The Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
4.National Laboratory for Marine Science and Technology, Qingdao 266071, China
5.The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
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摘要:

为了提升海上油膜与其他目标的可分离程度,提出基于特征值分解的一种新的极化特征G,该特征不仅能够反映集合中不同目标之间的极化状态,还能够描述不同散射类型在统计意义上的不纯度。若某个区域中去极化状态越弱,不纯度越低,则该区域中新极化特征G的值越低。利用两景Radarsat-2全极化SAR (Synthetic Aperture Radar)影像对新特征的有效性进行实验验证。结果表明:海水具有较小的特征值,油膜具有较大的特征值,生物膜的特征值介于两者之间。且与span、αˉ、P、A、CPD等5种经典的极化特征相比,新特征在图像对比度、局部标准偏差及概率密度曲线等三个指标上均有更好的表现,不仅能区分生物膜(植物油模拟)与原油,且具有更好的抑噪性。

关键词: Radarsat-2 SAR极化特征特征值分解不纯度溢油检测    
Abstract:

In order to improve the separability of oil film and other targets, a new polarization feature G based on eigenvalue and eigenvector decomposition is proposed. The new feature can not only reflect the polarization states between different targets in the corresponding set, but also has the ability to describe the statistical information impurities of the different scattering types. If the depolarization state was weaker, the impurities were smaller, then the value of the new polarimetric feature G in the specific region would be lower. Two sets of Radarsat-2 fully Pol-SAR (Polarimetric Synthetic Aperture Radar) data are used to verify the validity of the new feature G. The results show that there is a small eigenvalue in the seawater, a large eigenvalue in the oil film, the eigenvalue of the biofilm is between the oil film and seawater. In addition, the new feature G have better performance than span, αˉ, P, A and CPD in the image contrast, local standard deviation and probability density curve, which proves that the new feature G not only can distinguish bio-film(simulated by plant oil) and crude oil, but also has a good noise suppression ability.

Key words: Radarsat-2 SAR    Polarization feature    Eigenvalue decomposition    Impurity    Oil spill detection
收稿日期: 2019-05-08 出版日期: 2020-09-15
ZTFLH:  TP79  
基金资助: 国家重点研发计划(2017YFC1405600);国家自然基金委-山东省联合基金重点项目(U190621);国家自然科学基金项目(41772350)
通讯作者: 宋冬梅     E-mail: 328684934@qq.com;songdongmei@upc.edu.cn
作者简介: 任慧敏(1994-),女,新疆克拉玛依人,硕士研究生,主要从事极化SAR图像处理与溢油识别方法研究。E?mail: 328684934@qq.com
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引用本文:

任慧敏,宋冬梅,王斌,甄宗晋,刘斌,张婷. 基于新极化特征参数的SAR海洋溢油检测[J]. 遥感技术与应用, 2020, 35(4): 934-942.

Huimin Ren,Dongmei Song,Bin Wang,Zongjin Zhen,Bin Liu,Ting Zhang. New Polarimetric Feature Parameter for Marine Oil Spill Detection in SAR Images. Remote Sensing Technology and Application, 2020, 35(4): 934-942.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0934        http://www.rsta.ac.cn/CN/Y2020/V35/I4/934

图1  Radarsat-2全极化成像模式中VV通道的强度图像
数据集数据一数据二
产品类型SLCSLC
拍摄日期2011-05-082011-06-08
成像时间UTC 12:01:25UTC 17:27:52
聚束方式FQ23FQ15
极化方式HH,VV,HV,VHHH,VV,HV,VH
空间像素4.73m×4.95m4.73m×4.81m
入射角41.9°~43.3°34.5°~36.1°
表1  两景Radarsat-2数据集的成像参数
图2  新特征G在数据集1上的提取结果
品种乳化油植物油原油
日期2011.06.072011.06.082011.06.08
时间12:154:108:23
体积/m3200.430
表2  数据集2中植物油、原油与乳化油的排放情况
图3  新极化特征G在数据集2上的提取结果
特征相关公式符号描述
spanspan=Shh2+Shv2+Svh2+Svv2Sxy:散射振幅SAR散射目标的总功率
αˉαˉ=i=13piαiαi:与散射机制相关相位表征场景的散射机制
PP=ss2+ss3+ss4ss1ssi:斯托克斯元素表征散射机制的确定性
AA=λ2-λ3λ2+λ3,λ1>λ2>λ3λi:相干矩阵的特征值旋转不变性参数
CPDCPD=ShhSvv*:平均相位不同散射机制CPD不同
表3  用于对比的极化特征参量
图4  新特征G与span、αˉ、P、A、CPD的特征提取结果(数据集1)
图5  新特征G与span、αˉ、P、A、CPD的特征提取结果(数据集2)
图6  新特征G与span、αˉ、P、A、CPD在数据集1上的概率密度曲线.
图7  新特征G与span、αˉ、P、A、CPD在数据集2上的概率密度曲线.
极化特征G(本文提出)spanαˉPACPD
图像对比度Ci0.278 10.111 50.164 90.212 90.128 80.678 2
表4  油膜与海水的图像对比度(数据集1)
极化特征G(本文提出)spanαˉPACPD
图像对比度Ci0.376 70.124 40.011 00.181 40.165 60.230 4
表5  原油与植物油的图像对比度(数据集2)
局部标准偏差STD数据集1数据集2
油膜海水原油植物油
G(本文)0.014 40.048 70.062 80.055 2
span0.626 10.780 81.031 21.444 2
αˉ3.025 12.930 23.017 22.346 7
P0.046 80.088 40.123 60.077 4
A0.057 40.073 90.107 50.108 3
CPD1.585 70.180 50.442 50.143 0
表6  油膜、海水与植物油在两个数据集上的局部标准偏差
图8  新特征G在两个数据集上的阈值分割结果
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