基于SAR极化比和纹理特征的海面溢油识别方法

陈韩; 谢涛; 方贺; 孟雷; 赵立; 艾润冰

doi:10.3969/j.issn.0253-4193.2019.09.017

基于SAR极化比和纹理特征的海面溢油识别方法

doi: 10.3969/j.issn.0253-4193.2019.09.017

陈韩^1,,
谢涛^{2, 3, ,},
方贺¹,
孟雷⁴,
赵立¹,
艾润冰¹

1.
南京信息工程大学海洋科学学院，江苏南京 210044
2.
青岛海洋科学与技术试点国家实验室区域海洋动力学与数值模拟功能实验室，山东青岛 266237
3.
南京信息工程大学遥感与测绘工程学院，江苏南京 210044
4.
北京市511信箱，北京 100094

基金项目: 国家自然科学基金项目（41776181）；国家重点研发计划项目（2016YFC1401007）；全球变化研究国家重大科学研究计划项目（2015CB953901）；江苏省研究生科研创新计划（KYCX18_1012）。

详细信息

作者简介:
陈韩（1995—），男，江苏省南通市人，主要从事海洋环境遥感研究。E-mail: chenhan_0206@foxmail.com

通讯作者:
谢涛（1973—），男，湖南省张家界市人，教授，博士生导师，主要从事海洋微波遥感、极地卫星遥感等研究。E-mail：xtplqk@126.com

中图分类号: TP79
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- 文章访问数: 483
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- 被引次数: 0
出版历程
- 收稿日期: 2018-09-08
- 修回日期: 2018-11-25
- 网络出版日期: 2021-04-21
- 刊出日期: 2019-09-25

Sea surface oil spill identification method based on SAR polarization ratio and texture feature

Chen Han^1
,,
Xie Tao^{2, 3
, ,},
Fang He¹,
Meng Lei⁴,
Zhao Li¹,
Ai Runbing¹

1.
School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3.
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
4.
Beijing City 5111 Mailbox, Beijing 100094, China

摘要

摘要: 针对海洋表面SAR影像的特点，采用基于灰度共生矩阵的纹理特征方法是提取海面溢油信息的常用方法，但实际海洋表面复杂的信息使得SAR图像上产生类似溢油现象的暗斑区域，这导致在利用纹理特征方法提取溢油信息时存在虚警率，降低了溢油信息的提取精度。基于RADARSAT-2 SAR四极化影像，本文提出基于SAR极化比影像的纹理特征识别方法对海面油膜进行识别提取。结果显示，基于SAR极化比影像的纹理特征识别方法可以有效且准确地提取海面溢油信息，相比于VV极化影像的纹理特征识别方法，溢油监测过程中的虚警率降低了17.96%，溢油监测总体精度达到96.83%。
- 合成孔径雷达 /
- 溢油识别 /
- 极化比 /
- 纹理特征
Abstract: Aiming at the characteristics of SAR images on the ocean surface, the texture feature method based on gray level co-occurrence matrix is a common method for extracting oil spill information from the sea surface, but the complex information on the actual ocean surface makes the SAR image produce a dark spot area similar to the oil spill phenomenon. The false alarm rate is obtained when the oil feature information is extracted by the texture feature method, and the extraction precision of the oil spill information is reduced. Based on the RADARSAT-2 SAR quadratic polarization image, this paper proposes a texture feature recognition method based on SAR polarization ratio image to identify and extract the oil film on the sea surface. The results show that the texture feature recognition method based on SAR polarization ratio image can effectively and accurately extract the oil spill information on the sea surface. Compared with the texture feature recognition method of VV polarization image, the false alarm rate in the oil spill monitoring process is reduced by 17.96 %, the overall accuracy of oil spill monitoring reached 96.83%.
- synthetic aperture radar /
- oil spill identification /
- polarization ratio /
- texture feature

HTML全文

图 1 墨西哥湾溢油SAR图像

a.HH极化，b.VV极化；成像时间为2010年5月8日12时25分

Fig. 1 Gulf of Mexico oil spill SAR images

a. HH polarization, b. VV polarization; imaging time is 12:25 on May 8, 2010

下载: 全尺寸图片幻灯片

图 2 墨西哥湾溢油SAR图像

a.HH极化，b.VV极化；成像时间为2010年5月8日12时01分

Fig. 2 Gulf of Mexico oil spill SAR images

a. HH polarization, b. VV polarization; imaging time is 12:01 on May 8, 2010

下载: 全尺寸图片幻灯片

图 3 墨西哥湾溢油SAR极化比图像

a.原始数据成像时间2010年5月8日12时25分；b.原始数据成像时间2010年5月8日12时01分

Fig. 3 Gulf of Mexico oil spill SAR polarization ratio image

a.SAR data imaging at 12:25 on May 8, 2010; b.SAR data imaging at 12:01 on May 8, 2010

下载: 全尺寸图片幻灯片

图 4 两种方法的检测溢油流程

Fig. 4 Flow chart of oil spill detection by two ways

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图 5 墨西哥湾油识别结果对比

a.VV极化图像的能量特征向量图; b.PR图像的能量特征向量图; c.VV极化图像的同质性特征向量图; d.PR图像的同质性特征向量图；原始数据成像时间为2010年5月8日12时25分

Fig. 5 Comparison of Gulf of Mexico oil identification results

a. Energy texture image of the VV polarization image; b. energy texture image of the PR image; c. homomorphic texture image of the VV polarization image; d. homogeneity texture image of the PR image; the original data imaging time is 12:25 on May 8, 2010

下载: 全尺寸图片幻灯片

图 6 墨西哥湾油识别结果对比

a.VV极化图像的能量特征向量图; b.PR图像的能量特征向量图; c.VV极化图像的同质性特征向量图; d.PR图像的同质性特征向量图; 原始数据成像时间为2010年5月8日12时01分

Fig. 6 Comparison of Gulf of Mexico oil identification results

下载: 全尺寸图片幻灯片

图 7 分析区域

白色实线表示图8中使用的样带，原始数据成像时间2010年5月 8日12时25分

Fig. 7 Analysis of regional

The white solid line represents a transect used in Fig. 8, SAR data imaging at 12:25 on May 8, 2010

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图 8 图7中所示的横断线上的同质性折线图

Fig. 8 Plot of homomorphic value, across the transect shown in Fig. 7

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图 9 墨西哥湾溢油图像

a.原始数据成像时间2010年5月8日12时25分; b.原始数据成像时间2010年5月8日12时01分

Fig. 9 Images of the Gulf of Mexico oil spill

a. Original data imaging at 12:25 on May 8, 2010; b. original data imaging at 12:01 on May 8, 2010

下载: 全尺寸图片幻灯片

表 1 RADARSAT-2卫星和传感器参数

Tab. 1 RADARSAT-2 satellite and sensor parameters

轨道	轨道高度/km	重量/kg	倾角/（°）	运行周期/min	重访周期/d
太阳同步轨道(晨昏)	798	2 750	98.6	100.7	24

每天轨道数	卫星过境当地时间	极化方式	光束入射角度/（°）	分辨率/m	幅宽/km
14	约早6点，晚6点	HH、VV、HV、VH	18～50	3～100	20～500

下载: 导出CSV

表 2 常用纹理特征公式及特性

Tab. 2 Common texture feature formulas and characteristics

纹理特征	公式	特性
相关度	$\begin{array}{l} COR = \displaystyle\mathop \sum \limits_i \mathop \sum \limits_j \frac{{\left( {i - \mu } \right)\left( {j - \mu } \right)}}{{{\sigma ^2}}}p\left( {i,j,d,\theta } \right) \end{array}$	相关度反映图像局部灰度相关性
对比度	$CON = \displaystyle\mathop \sum \limits_i \mathop \sum \limits_j {\left( {i - j} \right)^2}p{\left( {i,j,d,\theta } \right)^2}$	对比度反映图像的清晰度和纹理的沟纹深浅
同质性	$HOM = \displaystyle\mathop \sum \limits_i \mathop \sum \limits_j \frac{1}{{1 + {{\left( {i - j} \right)}^2}}}p\left( {i,j,d,\theta } \right)$	同质性反映图像的均匀程度
能量	$ASM = \displaystyle\mathop \sum \limits_i \mathop \sum \limits_j p{\left( {i,j,d,\theta } \right)^2}$	能量反映图像灰度分布的均匀程度和纹理粗细程度

下载: 导出CSV

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