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基于极化G0分布和MRF的多视PolSAR图像迭代分类方法 被引量：3

Iterative Classification for Multi-look PolSAR Images Based on Polarimetric G0 Distribution and MRF
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摘要 提出了一种多视极化合成孔径雷达(PolSAR)图像的迭代分类方法。首先利用极化G0分布描述多视极化协方差矩阵的统计特性,并进行初始的最大似然分类,然后利用马尔可夫随机场(MRF)估计像素类标号的先验概率,最后根据MAP(最大后验概率)准则对PolSAR图像进行分类。整个分类流程迭代进行。分类结果表明该方法精度高,收敛速度快。利用NASA/JPL获取的4视AIRSAR实测数据验证了本文方法的有效性。 An iterative classification scheme of multi - look polarimetric synthetic aperture radar（PolSAR） images is presented here. Firstly. the polarimetric GO distribution is employed to describe the statistical characteristics of polarimetric covariance matrices. Then, an initial classification is provided by the maximum-likelihood classifier. Next, the Markov random field（MRF） is used to estimate the prior probabilities of class labels of pixels. Finally, the method classifies PolSAR images according to the MAP （maximum a posteriori ） criterion. The whole classification procedure is implemented iteratively. The classification results show that the method has high performance in accuracy and convergence. Experimental results using NASA/JPL 4-look AIRSAR data demonstrate the effectiveness of the proposed method.
作者 周晓光 贺志国 匡纲要 万建伟
机构地区 国防科技大学电子科学与工程学院
出处 《宇航学报》 EI CAS CSCD 北大核心 2009年第1期276-281,共6页 Journal of Astronautics
基金 国家自然科学基金(60532070)
关键词 极化合成孔径雷达(PolSAR) 分类 极化G0分布 马尔可夫随机场(MRF) 最大后验概率(MAP) Polarimetric synthetic aperture radar （PolSAR） Classification Polarimetric GO distribution Markov random field （MRF） Maximum a posteriori （MAP）
分类号 TN957.52 [电子电信—信号与信息处理] TP753 [自动化与计算机技术—检测技术与自动化装置]
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参考文献14

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同被引文献36

  • 1付琨,孙真真,吴一戎.基于Beta-Prime统计模型和QGD分类器的SAR图像地物分类方法[J].电子学报,2003,31(z1):2163-2166. 被引量：2
  • 2刘向阳,许稼,彭应宁.极不均匀合成孔径雷达杂波建模及恒虚警检测[J].电子学报,2007,35(9):1617-1621. 被引量：7
  • 3Lee J, Grnnes M, Kwok R. Classification of multi-look po- larimetric SAR imagery based on complex Wishart distribu- tion [ J ]. International Journal of Remote Sensing, 1994, 15 (11) : 2299-2311.
  • 4Cloude S R, 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.
  • 5Frery A C, Correia A H, Freitas C C. Classifying multifre- quency fully polarimetric imagery with multiple sources of statistical evidence and contextual information [ J ]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45 (10) : 3098-3109.
  • 6Doulgeris A P, Anfinsen S N, Eltoft T. Classification with a non-gaussian model for PolSAR data[ J ]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10) : 2999-3009.
  • 7Zhang Lamei, Zou Bin, Zhang Junping, et al. Classification of polarimetric SAR image based on support vector machine using multiple-component scattering model and texture features[ C ]//2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar. Xi'an: IEEE Press, 2009: S05-808.
  • 8Ince T, Kiranyaz S, Gabbouj M. Classification of polarime- tric SAR images using evolutionary RBF networks [ C ]//2010 International Conference on Pattern Recognition. Istan- bul: IEEE Press, 2009: 4324-4327.
  • 9Freitas C C, Frery A C, Correia A H. The polarimetric G distribution for SAR data analysis [ J ]. Environmetrics, 2005, 16(1) : 13-31.
  • 10Aniinsen S N, Eltoit T. Application of the matrix-variate mellin transform to analysis of polarimetric radar images [ J]. IEEE Transactions on Geoscience and Remote Sens- ing, 2011,49,(7) : 1-15.

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宇航学报
2009年 第1期

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