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极化合成孔径雷达图像相干斑抑制和分类方法综述

聂祥丽 黄夏渊 张波 乔红

聂祥丽, 黄夏渊, 张波, 乔红. 极化合成孔径雷达图像相干斑抑制和分类方法综述. 自动化学报, 2019, 45(8): 1419-1438. doi: 10.16383/j.aas.c180097
引用本文: 聂祥丽, 黄夏渊, 张波, 乔红. 极化合成孔径雷达图像相干斑抑制和分类方法综述. 自动化学报, 2019, 45(8): 1419-1438. doi: 10.16383/j.aas.c180097
NIE Xiang-Li, HUANG Xia-Yuan, ZHANG Bo, QIAO Hong. Review on PolSAR Image Speckle Reduction and Classification Methods. ACTA AUTOMATICA SINICA, 2019, 45(8): 1419-1438. doi: 10.16383/j.aas.c180097
Citation: NIE Xiang-Li, HUANG Xia-Yuan, ZHANG Bo, QIAO Hong. Review on PolSAR Image Speckle Reduction and Classification Methods. ACTA AUTOMATICA SINICA, 2019, 45(8): 1419-1438. doi: 10.16383/j.aas.c180097

极化合成孔径雷达图像相干斑抑制和分类方法综述

doi: 10.16383/j.aas.c180097
基金项目: 

国家自然科学基金 91648205

国家自然科学基金 U1435220

国家自然科学基金 61802408

国家自然科学基金 61602483

详细信息
    作者简介:

    黄夏渊  中国科学院自动化研究所助理研究员.2016年获得中国科学院数学与系统科学研究院博士学位.主要研究方向为图像分类与机器学习.E-mail:xiayuan.huang@ia.ac.cn

    张波   中国科学院数学与系统科学研究院研究员.1992年获得英国斯特拉斯克莱德大学博士学位.主要研究方向为散射与反散射问题, 机器学习和模式识别.E-mail:b.zhang@amt.ac.cn

    乔红   中国科学院自动化研究所研究员.1995年获得英国德蒙特福德大学博士学位.主要研究方向为机器人与机器学习.E-mail:hong.qiao@ia.ac.cn

    通讯作者:

    聂祥丽   中国科学院自动化研究所助理研究员.2015年获得中国科学院数学与系统科学研究院博士学位.主要研究方向为图像处理与机器学习.本文通信作者.E-mail:xiangli.nie@ia.ac.cn

Review on PolSAR Image Speckle Reduction and Classification Methods

Funds: 

National Natural Science Foundation of China 91648205

National Natural Science Foundation of China U1435220

National Natural Science Foundation of China 61802408

National Natural Science Foundation of China 61602483

More Information
    Author Bio:

      Assistant professor at the Institute of Automation, Chinese Academy of Sciences. She received her Ph. D. degree from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2016. Her research interest covers image classification and machine learning

      Professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. He received his Ph. D. degree from University of Strathclyde, Glasgow, U.K. in 1992. His research interest covers direct and inverse scattering problems, machine learning and pattern recognition

      Professor at the Institute of Automation, Chinese Academy of Sciences. She received her Ph. D. degree from De Montfort University, Leicester, U.K. in 1995. Her research interest covers robotics and machine learning

    Corresponding author: NIE Xiang-Li   Assistant professor at the Institute of Automation, Chinese Academy of Sciences. She received her Ph. D. degree from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2015. Her research interest covers image processing and machine learning. Corresponding author of this paper
  • 摘要: 极化合成孔径雷达(Polarimetric synthetic aperture radar,PolSAR)是一种多参数、多通道的微波成像系统,在农林业、地质、海洋和军事等领域有着广泛的应用前景.PolSAR图像的相干斑抑制和分类是数据解译的重要环节,已经成为遥感领域的研究热点.本文综述了现有PolSAR图像的相干斑噪声抑制和分类方法并进行展望.首先,简要介绍了PolSAR系统的主要进展和应用;然后,对PolSAR图像相干斑抑制的评价指标和方法进行综述并对几种代表性方法进行了实验对比;接下来,对PolSAR图像的特征进行分析归纳,分别对有监督、无监督和半监督的PolSAR分类方法进行总结并给出了几种有监督分类方法的实验比较;最后,对PolSAR图像相干斑抑制和分类方法未来可能的研究方向进行了思考和讨论.
    1)  本文责任编委 谢永芳
  • 图  1  不同PolSAR数据相干斑抑制方法的视觉对比结果

    Fig.  1  Visual comparison results of different PolSAR data speckle reduction methods

    图  2  PolSAR图像分类方法归类

    Fig.  2  The category of PolSAR image classification methods

    图  3  不同PolSAR图像分类方法的对比结果

    Fig.  3  Comparison results of different PolSAR image classification methods

    表  1  各类PolSAR数据相干斑抑制方法比较表

    Table  1  Comparison of different types of PolSAR data speckle reduction methods

    相干斑抑制方法 代表性算法 优点 不足
    空域滤波方法 修正的Lee、IDAN、改进的sigma滤波 操作简单、计算速度快 空间分辨率低、边缘模糊、细节信息丢失
    非局部均值方法 Pretest、随机散度滤波、NL-SAR 去噪效果显著、结构特征保持得好 计算速度很慢、出现像素块状效应
    变分方法 WisTV、WisNLTV、MuLoG算法 去噪显著、速度快、边缘保持得好 弱目标丢失、平坦区域出现阶梯效应
    下载: 导出CSV

    表  2  不同PolSAR数据相干斑抑制方法的量化比较表

    Table  2  Quantitative comparison of different PolSAR data speckle reduction methods

    指标/方法 修正的Lee滤波 SMBF滤波 NL-SAR方法 WisNLTV方法
    ENL 13.8698 10.9144 11.5435 10.7267
    EPD-ROA水平方向 0.6863 0.7356 0.7505 0.7533
    EPD-ROA垂直方向 0.7472 0.7957 0.8046 0.8109
    下载: 导出CSV

    表  3  不同PolSAR图像分类方法的分类精度比较(%)

    Table  3  Classification accuracy of different PolSAR image classification methods (%)

    类别/方法 SWC SVM LPP RV-CNN CV-CNN
    城市区域 49.4 78.7 82.5 85.7 91.3
    林地区域 80.2 93.8 93.8 85.2 92.2
    其他区域 95.6 95.9 96.5 93.4 94.6
    总精度 81.2 91.2 92.5 89.9 93.4
    下载: 导出CSV
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