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极化SAR数据的细节保持分割 被引量：3

Detail-preserving segmentation of polarimetric synthetic aperture radar data
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摘要 提出一种保持场景局部细节的极化合成孔径雷达数据非监督分割方法。该方法将传统的基于极化目标分解的极化SAR分类方法与基于马尔可夫随机场的分类方法相结合,利用迭代条件模型方法得到分割结果。为保持细节特征,同时又能对各向同性区域进行平滑,利用总功率(span)图像的场景均匀性对分类结果进行修正。实验结果表明,与已有方法相比,该方法在细节保持方面有一定改进。 An unsupervised detail-preserving segmentation method of polarimetric synthetic aperture radar （POLSAR） data is proposed. This method combines the classical POLSAR data classification method based on polarimetric target decomposition and the classification method based on Markov random field （MRF）. An Iterated conditional modes （ICM） method is used to obtain the segmentation results. In order to preserve details and smooth the homogeneous regions, the classification result is modified by using the scene homogeneity of the total power （span） image. Experiment results indicate that the proposed method achieves some improvements for detail preservation compared with existed methods.
作者 张涛 胡风明 杨汝良
机构地区 中国科学院电子学研究所 中国科学院研究生院
出处 《系统工程与电子技术》 EI CSCD 北大核心 2009年第10期2372-2375,共4页 Systems Engineering and Electronics
关键词 极化合成孔径雷达 细节保持 马尔可夫随机场 分割 polarimetric synthetic aperture radar detail preserving Markov random field segmentation
分类号 TN957.51 [电子电信—信号与信息处理]
作者简介 张涛（1980-），男，博士研究生，主要研究方向为极化SAR图像处理。E—mail：tzang@sina．com
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参考文献16

  • 1Beaulieu J M, Touzi R. Segmentation of textured polarimetric SAR scenes by likelihood approximation[J]. IEEE Trans. on Geoscience Remote Sensing, 2004, 42 (10) : 2063 - 2072.
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同被引文献44

  • 1王文光,王俊,毛士艺.一种基于差异度的极化SAR图像迭代分类方法[J].电子与信息学报,2006,28(11):2007-2010. 被引量：9
  • 2Lee J S, Grunes M R, Kwok R. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution [J]. International Journal of Remote Sensing, 1994,15 (11) : 2299 - 2311.
  • 3Wu Y H, Ji K F, Yu W X, et al. Region-based classification ofpolarimetric SAR images using Wishart MRF[J]. IEEE Trans. on Geoscience and Remote Sensing Letters, 2008,5(4): 668 - 672.
  • 4Cloude S R, Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Trans. on Geoscience and Remote Sensing, 1996,34(2) :498 - 518.
  • 5Freeman A, Durden S. A three-component scattering model for polarimetric SAR data [J]. IEEE Trans. on Geoscience and Remote Sensing ,1998,36(3) :963 - 973.
  • 6Lee J S, Grunes M R, Ainsworth T L, et al. Unsupervised clas sification using polarimetrie decomposition and the complex Wishart classifier[J]. IEEE Trans. on Geoscience and Remote Sensing,1999,37(5) :2249 - 2257.
  • 7Pottier E, Lee J S. Application of the H/A/alpha polarimetric decom- position theorem for unsupervised classification of fully polarimetric SAR data based on the wishart distribution[C]//Proc, of the Committee on Earth Observing Satellites SAR Workshop, 2000 : 335 - 340.
  • 8Lee J S, Grunes M R, Pottier E. Unsupervised terrain classification preserving polarimetric scattering characteristics [J]. IEEE Trans. on Geoscience and Remote Sensing, 2004,42 (4) :722 - 731.
  • 9Cao F, Hong W, Wu Y R, et al. An unsupervised segmentation with an adaptive number of clusters using the SPAN/H/a/A space and the complex Wishart clustering for fully polarimetric SAR data analysis[J]. IEEE Trans. on Geoscience and Remote Sensing ,2007,45(11) :3454 - 3467.
  • 10Kaan E, Ian G C, Rabab K W. Segmentation and classification of polarimetric SAR data using spectral graph partitioning[J]. IEEE Trans. on Geoscience and Remote Sensing ,2010,48(1) :164 - 174.

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系统工程与电子技术
2009年 第10期

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