留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

高分辨率极化SAR影像KummerU分布非监督分类方法

朱腾 何汉武 黄铁兰 张坡

朱腾, 何汉武, 黄铁兰, 张坡. 高分辨率极化SAR影像KummerU分布非监督分类方法[J]. 华南师范大学学报(自然科学版), 2020, 52(1): 85-90. doi: 10.6054/j.jscnun.2020013
引用本文: 朱腾, 何汉武, 黄铁兰, 张坡. 高分辨率极化SAR影像KummerU分布非监督分类方法[J]. 华南师范大学学报(自然科学版), 2020, 52(1): 85-90. doi: 10.6054/j.jscnun.2020013
ZHU Teng, HE Hanwu, HUANG Tielan, ZHANG Po. The Unsupervised Classification Method with KummerU Distribution of High Resolution PolSAR Images[J]. Journal of South China normal University (Natural Science Edition), 2020, 52(1): 85-90. doi: 10.6054/j.jscnun.2020013
Citation: ZHU Teng, HE Hanwu, HUANG Tielan, ZHANG Po. The Unsupervised Classification Method with KummerU Distribution of High Resolution PolSAR Images[J]. Journal of South China normal University (Natural Science Edition), 2020, 52(1): 85-90. doi: 10.6054/j.jscnun.2020013

高分辨率极化SAR影像KummerU分布非监督分类方法

doi: 10.6054/j.jscnun.2020013
基金项目: 

国家重点研发计划项目 2018YFB1004902

广东省科技计划项目 2017B010110008

广东省教育厅基础应用研究重大项目 2017GKZDXM002

详细信息
    通讯作者:

    黄铁兰, 高级研究员, Email:707549941@qq.com

  • 中图分类号: P237

The Unsupervised Classification Method with KummerU Distribution of High Resolution PolSAR Images

  • 摘要: 针对高分辨率极化SAR数据特征分布不再符合同质区域假设, 进而导致基于统计分布的极化SAR影像非监督分类方法精度下降的问题, 将具有广泛适用性的KummerU分布嵌入粒子群寻优聚类算法, 提出了新的极化SAR影像非监督分类算法(PSO-KummerU方法):首先基于极化SAR统计特征对数据进行初分类, 然后采用极化SAR统计特征与粒子群优化算法进一步进行聚类中心求解, 分类准则部分采用KummerU距离改进代替传统的Wishart距离度量准则; 采用3种非监督分类方法(H/α-Wishart、PSO-Wishart、PSO-KummerU方法)进行分类对比实验.实验结果表明:基于KummerU分布的PSO-KummerU方法与采用Wishart距离的聚类方法相比, 目视效果明显改进, 整体分类精度提高14%以上.
  • 图  1  PSO-KummerU方法分类流程

    Figure  1.  The workflow of the PSO-KummerU classification method

    图  2  海南陵水实验区的PauliRGB合成图与真实地物参照

    Figure  2.  The PauliRGB and real terrain reference of Hainan Lingshui experimental area

    图  3  海南陵水实验区分类结果

    Figure  3.  The classification results of Hainan Lingshui experimental area

    表  1  海南实验数据分类精度对比

    Table  1.   The comparison of classification accuracy of Hainan data

    分类方法 分类精度/% 总体精度/% Kappa系数
    辣椒田 幼苗耕地 茂密作物 裸土 水体
    H/α-Wishart 48.85 58.93 80.32 65.10 99.58 61.20 0.532 6
    PSO-Wishart 51.16 61.34 78.69 72.34 99.90 63.13 0.570 1
    PSO-KummerU 76.34 71.52 88.13 76.71 98.80 77.63 0.726 5
    下载: 导出CSV
  • [1] 马姣娇, 牛安逸, 徐颂军, 等.基于地学信息图谱的珠海淇澳岛土地利用格局分析[J].华南师范大学学报(自然科学版), 2018, 50(2):77-85. http://journal-n.scnu.edu.cn/article/id/4099

    MA J J, NIU A Y, XIU S J, et al. Analysis of land use pattern based on Geo-information TUPU in Zhuhai Qi'ao Island[J]. Journal of South China Normal University(Natural Science Edition), 2018, 50(2):77-85. http://journal-n.scnu.edu.cn/article/id/4099
    [2] YU P, QIN A K, CLAUSI D A. Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4):1302-1317. doi: 10.1109/TGRS.2011.2164085
    [3] LEE J S, GRUNES M R, AINSWORTH T L. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J]. IEEE Transactions on Geo-science and Remote Sensing, 1999, 37(5):2249-2258. doi: 10.1109/36.789621
    [4] 杨杰, 郎丰铠, 李德仁.一种利用Cloude-Pottier分解和极化白化滤波的全极化SAR图像分类算法[J].武汉大学学报(信息科学版), 2011, 36(1):104-107. http://www.cnki.com.cn/Article/CJFDTotal-WHCH201101023.htm

    YANG J, LANG F K, LI D R. An unsupervised wishart classification for fully polarimetric SAR image based on cloude-pottier decomposition and polarimetric whitening filter[J]. Geomatics and Information Science of Wuhan University, 2011, 36(1):104-107. http://www.cnki.com.cn/Article/CJFDTotal-WHCH201101023.htm
    [5] TISON C, NICOLAS J M, TUPIN F, et al. A new statistical model for markovian classification of urban areas in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10):2046-2057. doi: 10.1109/TGRS.2004.834630
    [6] BOMBRUN L, VASILE G, GAY M, et al. Hierarchical segmentation of polarimetric SAR images using heterogeneous clutter models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2):726-737. doi: 10.1109/TGRS.2010.2060730
    [7] HARANT O, BOMBRUN L, GAY M, et al. Segmentation and classification of polarimetric SAR data based on the KummerU distribution[C]//Proceedings of the Fourth International Workshop on Science and Application of SAR Polarimetry & Polarimetric Interferometry Poiinsar. Frascati, Italy: [s.n.], 2009: 668-673.
    [8] 邹鹏飞, 李震, 田帮森.高分辨率极化SAR图像水平集分割[J].中国图象图形学报, 2014, 19(12):1829-1835. doi: 10.11834/jig.20141215

    ZOU P F, LI Z, TIAN B S. High-resolution PolSAR image level set segmentation[J]. Journal of Image and Graphics, 2014, 19(12):1829-1835. doi: 10.11834/jig.20141215
    [9] 石俊飞, 林耀海, 刘璐.基于KummerU和MRF的极化SAR分类算法研究[J].火控雷达技术, 2015, 44(4):51-54. doi: 10.3969/j.issn.1008-8652.2015.04.012

    SHI J F, LIN Y H, LIU L. Study on polarmetric SAR classification algorithm based on KummerU and MRF[J]. Fire Control Radar Technology, 2015, 44(4):51-54. doi: 10.3969/j.issn.1008-8652.2015.04.012
    [10] 管翔辉, 秦先祥.一种基于KummerU分布的SAR图像统计建模方法[J].科学技术与工程, 2016, 16(28):235-240. doi: 10.3969/j.issn.1671-1815.2016.28.043

    GUAN X H, QIN X X. A statistical modeling method for SAR images based on KummerU distribution[J]. Science Technology and Engineering, 2016, 16(28):235-240. doi: 10.3969/j.issn.1671-1815.2016.28.043
    [11] 李林宜, 李德仁.粒子群优化算法在遥感影像增强中的应用[J].测绘科学技术学报, 2010, 27(2):116-119. doi: 10.3969/j.issn.1673-6338.2010.02.011

    LI L Y, LI D R. Research on particle swarm optimization in remote sensing image enhancement[J]. Journal of Geomatics Science and Technology, 2010, 27(2):116-119. doi: 10.3969/j.issn.1673-6338.2010.02.011
    [12] YI H, YANG J, LI P, et al. A PolSAR image segmentation algorithm based on scattering characteristics and the revised wishart distance[J]. Sensors, 2018, 18(7):2262-2271. doi: 10.3390/s18072262
    [13] ZHANG Y, ZOU H X, LUO T C, et al. A fast superpixel segmentation algorithm for PolSAR images based on edge refinement and revised wishart distance[J]. Sensors, 2016, 16(10):1687-1695. doi: 10.3390/s16101687
    [14] WANG W, XIANG D L, BAN Y F, et al. Superpixel segmentation of polarimetric SAR images based on integrated distance measure and entropy rate method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(9):1-14. doi: 10.1109/JSTARS.2017.2751699
    [15] 秦先祥.基于广义Gamma分布的SAR图像统计建模及应用研究[D].长沙: 国防科学技术大学, 2015. http://cdmd.cnki.com.cn/Article/CDMD-90002-1017834267.htm

    QIN X X. Research on statistical modeling of SAR images and its application based on generalized Gamma distribution[D]. Changsha: National University of Defense Technology, 2015. http://cdmd.cnki.com.cn/Article/CDMD-90002-1017834267.htm
    [16] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
    [17] CAI J H, ZHANG J F, ZHAO X J. Research on two-stage fuzzy clustering method for spectrum data based on PSO[J]. Spectroscopy and Spectral Analysis, 2009, 29(4):1137-1141. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gpxygpfx200904061
    [18] AMOON M, REZAI-RAD G A, DALIRI M R. PSO-based optimal selection of zernike moments for target discrimination in high-resolution SAR imagery[J]. Journal of the Indian Society of Remote Sensing, 2014, 42(3):483-493. doi: 10.1007/s12524-013-0344-6
    [19] ZHU T, YU J, LI X J, et al. PolSAR image classification using fuzzy logic in the H/α-Wishart algorithm[J]. Journal of Applied Remote Sensing, 2015, 9(1):096098/1-18.
  • 加载中
图(3) / 表(1)
计量
  • 文章访问数:  1554
  • HTML全文浏览量:  751
  • PDF下载量:  10
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-10-17
  • 刊出日期:  2020-02-25

目录

    /

    返回文章
    返回