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合成孔径雷达海面溢油探测研究进展

李煜 陈杰 张渊智

李煜, 陈杰, 张渊智. 合成孔径雷达海面溢油探测研究进展[J]. 电子与信息学报, 2019, 41(3): 751-762. doi: 10.11999/JEIT180468
引用本文: 李煜, 陈杰, 张渊智. 合成孔径雷达海面溢油探测研究进展[J]. 电子与信息学报, 2019, 41(3): 751-762. doi: 10.11999/JEIT180468
Yu LI, Jie CHEN, Yuanzhi ZHANG. Progress in Research on Marine Oil Spills Detection Using Synthetic Aperture Radar[J]. Journal of Electronics & Information Technology, 2019, 41(3): 751-762. doi: 10.11999/JEIT180468
Citation: Yu LI, Jie CHEN, Yuanzhi ZHANG. Progress in Research on Marine Oil Spills Detection Using Synthetic Aperture Radar[J]. Journal of Electronics & Information Technology, 2019, 41(3): 751-762. doi: 10.11999/JEIT180468

合成孔径雷达海面溢油探测研究进展

doi: 10.11999/JEIT180468
基金项目: 国家重点研发计划(2016YFB0501501),国家自然科学基金(41706201)
详细信息
    作者简介:

    李煜:男,1986年生,讲师,研究方向为遥感图像处理和模式识别

    陈杰:男,1973年生,教授,研究方向为合成孔径雷达系统建模和信号处理

    张渊智:男,1964年生,研究员,研究方向为微波和光学遥感

    通讯作者:

    张渊智 zhangyz@nao.cas.cn

  • 中图分类号: TN957.52

Progress in Research on Marine Oil Spills Detection Using Synthetic Aperture Radar

Funds: The National key Research and Development Project of China (2016YFB0501501), The National Natural Science Foundation of China (41706201)
  • 摘要:

    海洋溢油污染不仅严重威胁海洋生态安全、破坏海岸带环境,而且直接和间接地影响着广大人民群众的生活和健康以及区域社会经济的发展。合成孔径雷达因其具有全天候和高灵敏度的观测能力而成为海面油膜探测的主要手段之一。该文从SAR海面油膜探测的基本原理出发,介绍了单极化、全极化和紧缩极化SAR海面油膜探测技术的国内外最新研究进展,对该技术手段在实际应用中遇到的主要困难和挑战做了深入分析,最后总结展望了该技术未来发展的广阔前景。

  • 图  1  墨西哥湾“深水地平线”溢油事故[3]

    图  2  SAR成像几何示意图

    图  4  包含溢油区域的汕尾附近海域SAR后向散射VV通道图像(图像来自欧空局)

    图  3  雷达信号海面散射示意图

    表  1  常用单极化SAR油膜特征

    强度特征形态学特征纹理特征*环境特征
    油膜后向散射强度(${\mu _{{\rm{obj}}}}$) 面积(A) 同质性(Homogeneity) 距海岸距离
    油膜后向散射方差(${\sigma _{{\rm{obj}}}}$) 周长(P ) 对比度(Contrast) 距最近黑斑距离
    油膜周围后向散射(${\mu _{{\rm{sce}}}}$) 复杂度(C ) 差异度(Dissimilarity) 周围黑斑数量
    灰度比(${\mu _{{\rm{obj}}}}/{\mu _{{\rm{sce}}}}$) 不对称性 熵(Entropy) 周围船只数量
    方差比(${\sigma _{{\rm{obj}}}}/{\sigma _{{\rm{sce}}}}$) 欧拉数 均值(Mean)
    ISRI(${\mu _{{\rm{obj}}}}/{\sigma _{{\rm{obj}}}}$) 形状指数 方差(Variance)
    ISRO(${\mu _{{\rm{obj}}}}/{\sigma _{{\rm{sce}}}}$) 轴线长度 相关性(Correlation)
    油膜最小灰度值(MSV) 紧致度
    最大对比度(${\sigma _{{\rm{sce}}}}$-MSV)
    边缘梯度
    注:纹理特征通过灰度共生矩阵(Gray-Level Co-occurrence Matrix, GLCM)得到
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-06
  • 修回日期:  2018-11-15
  • 网络出版日期:  2018-12-17
  • 刊出日期:  2019-03-01

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