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基于聚类识别的极化SAR图像分类

魏志强 毕海霞

魏志强, 毕海霞. 基于聚类识别的极化SAR图像分类[J]. 电子与信息学报, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229
引用本文: 魏志强, 毕海霞. 基于聚类识别的极化SAR图像分类[J]. 电子与信息学报, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229
Zhiqiang WEI, Haixia BI. PolSAR Image Classification Based on Discriminative Clustering[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229
Citation: Zhiqiang WEI, Haixia BI. PolSAR Image Classification Based on Discriminative Clustering[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229

基于聚类识别的极化SAR图像分类

doi: 10.11999/JEIT180229
详细信息
    作者简介:

    魏志强:男,1974年生,博士,研究员,研究方向为雷达系统工程、图像处理和太赫兹技术

    毕海霞:女,1982年生,博士,高级工程师,研究方向为机器学习、遥感图像处理、大数据处理和通信技术

    通讯作者:

    魏志强  zqwei@fudan.edu.cn

  • 中图分类号: TP75

PolSAR Image Classification Based on Discriminative Clustering

  • 摘要: 该文提出一种基于判别式聚类框架的非监督极化SAR图像分类算法,利用判别式监督分类技术实现非监督聚类。为实现该算法,定义了一个结合softmax回归模型和马尔科夫随机场光滑性约束的能量函数。该模型中,像素类标和分类器均为需要优化的未知变量。该算法从基于 ${H / {\bar \alpha }}$ 目标极化分解和K-Wishart极化统计分布而产生的初始化类标开始,交替迭代优化分类器和类标的能量函数,从而实现对分类器和类标的求解。真实极化SAR数据上的实验结果证明了该算法的有效性和先进性。
  • 图  1  基于判别式的PolSAR图像分类方法流程

    图  2  本文所使用的两幅原始极化SAR图像

    图  3  两幅极化SAR图像的真实地物标注

    图  4  光滑系数和迭代次数对分类结果的影响

    图  5  使用不同方法对Flevland地区分类结果

    图  6  使用不同方法对Oberpfaffenhofen地区的分类结果

    表  1  本文算法所使用的特征

    极化特征分类 标识 物理描述
    极化矩阵及其数学变换 ${{{T}}_{ij}}(i,j = 1,2,3,i \le j)$ 水平垂直线极化方式下的相干矩阵元素(模值与幅角)
    ${\rm{Lin}}45{{{T}}_{ij}}(i,j = 1,2,3,i \le j)$ +45°/–45°线极化方式下的相干矩阵元素(模值与幅角)
    ${\rm{Cir}}45{{{T}}_{ij}}(i,j = 1,2,3,i \le j)$ 左右旋圆极化方式下的相干矩阵元素(模值与幅角)
    $\frac{{{I_{{\rm{hv}}}}}}{{{I_{{\rm{hh}}}}}},\frac{{{I_{{\rm{hv}}}}}}{{{I_{{\rm{vv}}}}}},\frac{{{I_{{\rm{hh}}}}}}{{{I_{{\rm{vv}}}}}},\frac{{{I_{{\rm{rr}}}}}}{{{I_{{\rm{lr}}}}}},\frac{{{I_{{\rm{ll}}}}}}{{{I_{{\rm{lr}}}}}},\frac{{{I_{{\rm{ll}}}}}}{{{I_{{\rm{rr}}}}}},\frac{{{I_{{\rm{mn}}}}}}{{{I_{{\rm{mm}}}}}},\frac{{{I_{{\rm{mn}}}}}}{{{I_{{\rm{nn}}}}}},\frac{{{I_{{\rm{mm}}}}}}{{{I_{{\rm{nn}}}}}}$ 水平垂直线极化,+45°/–45°线极化以及
    左右旋圆极化方式下的强度比值
    SPAN 极化总功率
    目标分解特征 Pauli矩阵分解 Pauli分解参数
    ${P_{\rm{s}}},{P_{\rm{d}}},{P_{\rm{v}}},{\alpha _{\rm{L}}}$ Freeman分解参数
    $\bar \alpha ,H,A,\beta ,(1 - H)(1 - A),(1 - H)A,H(1 - A),HA$ ${H / {\bar \alpha }}$分解参数
    下载: 导出CSV

    表  2  不同方法在Flevoland地区数据上的分类准确率(%)

    方法/类别 空地 大麦 苜蓿 豌豆 土豆 甜菜 小麦 总分类准确率
    $H/\bar \alpha {\scriptsize{-}} {\rm{Wishart}}$ 99.94 86.60 84.63 88.50 81.44 84.00 82.48 85.59
    Wishart MRF 100.00 86.21 93.27 94.24 85.55 89.54 92.55 91.82
    Freeman Wishart 98.26 87.97 83.38 93.22 98.13 92.20 84.53 90.16
    功率熵和共极化率 99.44 89.85 75.22 85.92 85.79 91.01 87.89 88.30
    Wishart TMF 99.81 97.60 86.10 98.27 90.00 95.86 97.33 95.86
    本文算法 100.00 100.00 95.07 98.21 98.57 98.63 99.93 99.05
    下载: 导出CSV

    表  3  本文方法在Oberpfaffenhofen地区数据上的分类准确率(%)

    对象/类别 森林 道路 郊区 开阔地 总分类准确率
    ${H / {\bar \alpha }} {\scriptsize{-}} {\rm{Wishart}}$ 65.21 41.80 51.51 76.01 64.43
    Wishart MRF 74.43 55.52 42.28 72.93 67.26
    Freeman Wishart 97.71 18.13 16.80 87.85 73.35
    本文算法 93.65 64.70 60.78 84.41 82.25
    下载: 导出CSV
  • KONG J A, SWARTZ A A, YUEH H A, et al. Identification of terrain cover using the optimum polarimetric classifier[J]. Journal of Electromagnetic Waves and Applications, 1988, 2(2): 171–194.
    POTTIER E and SAILLARD J. On radar polarization target decomposition theorems with application to target classification by using network method[C]. Proceedings of the International Conference on Antennas and Propagation, York, UK, 1991, 1: 265–268.
    FUKUDA S and HIROSAWA H. Support vector machine classification of land cover: Application to polarimetric SAR data[C]. Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia, 2001, 1: 187–189.
    JIAO Licheng and LIU Fang. Wishart deep stacking network for fast PolSAR image classification[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3273–3286 doi: 10.1109/TIP.2016.2567069
    ZHOU Yu, WANG Haipeng, XU Feng, et al. Polarimetric SAR image classification using deep convolutional neural networks[J]. IEEE Geoscience Remote Sensing Letters, 2017, 13(12): 1935–1939 doi: 10.1109/LGRS.2016.2618840
    GAO Wei, YANG Jian, and MA Wenting. Land cover classification for polarimetric SAR images based on mixture models[J]. Remote Sensing, 2014, 6(5): 3770–3790 doi: 10.3390/rs6053770
    CLOUDE S R and POTTIER E. An entropy based classification scheme for land application of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68–78 doi: 10.1109/36.551935
    FREEMAN A and DURDEN S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963–973 doi: 10.1109/36.673687
    YAMAGUCHI Y, MORIYAMA T, ISHIDO M, et al. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699–1706 doi: 10.1109/TGRS.2005.852084
    XU Feng and JIN Yaqiu. Deorientation theory of polarimetric scattering targets and application to terrain surface classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(10): 2351–2364 doi: 10.1109/TGRS.2005.855064
    DOULEGERIS A P, ANFINSEN S N, and ELROFT T. Classification with a non-Gaussian model for PolSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 2999–3009 doi: 10.1109/TGRS.2008.923025
    徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136–148 doi: 10.12000/JR16130

    XU Feng, WANG Haipeng and JIN Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136–148 doi: 10.12000/JR16130
    钟能, 杨文, 杨祥立, 等. 基于混合Wishart模型的极化SAR图像非监督分类[J]. 雷达学报, 2017, 6(2): 136–148 doi: 10.12000/JR16133

    ZHONG Neng, YANG Wen, YANG Xiangli, et al. Unsupervised Classification for Polarimetric Synthetic Aperture RadarImages Based on Wishart Mixture Models[J]. Journal of Radars, 2017, 6(2): 136–148 doi: 10.12000/JR16133
    BACH F and HARCHAOUI Z. DIFFRAC: A discriminative and flexible framework for clustering[C]. Proceedings of Conference and Workshop on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2007: 49–56.
    SUN Jian and PONECE J. Learning discriminative part detectors for image classification and cosegmentation[C]. Proceedings of IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 2013: 3400–3407.
    ZHU Ciyou, BYRD R H, LU Peihuang, et al. L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization[J]. ACM Transaction Mathematical Software, 1997, 23(4): 550–560 doi: 10.1145/279232.279236
    WU Yonghui, JI Kefeng, YU Wenxian, et al. Region-based classification of polarimetric SAR images using Wishart MRF[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 668–672 doi: 10.1109/LGRS.2008.2002263
    LEE J S, GRUNES M R, Ainsworth T L, et al. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249–2258 doi: 10.1109/36.789621
    WANG Shuang, LIU Kun, PEI Jingjing, et al. Unsupervised classification of fully polarimetric SAR images based on scattering power entropy and copolarized ratio[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 622–626 doi: 10.1109/LGRS.2012.2216249
    LIU Gaofeng, LI Ming, WU Yan, et al. PolSAR image classification based on Wishart TMF with specific auxiliary field[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1230–1234 doi: 10.1109/LGRS.2013.2290066
    LEE J S, GRUMES M R, POTTIER E, et al. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722–731 doi: 10.1109/TGRS.2003.819883
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出版历程
  • 收稿日期:  2018-03-09
  • 修回日期:  2018-08-22
  • 网络出版日期:  2018-08-29
  • 刊出日期:  2018-12-01

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