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基于半正定约束的极化相似度最优模型匹配目标分解

范庆辉 卢红喜 保铮 肖春宝

范庆辉, 卢红喜, 保铮, 肖春宝. 基于半正定约束的极化相似度最优模型匹配目标分解[J]. 电子与信息学报, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468
引用本文: 范庆辉, 卢红喜, 保铮, 肖春宝. 基于半正定约束的极化相似度最优模型匹配目标分解[J]. 电子与信息学报, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468
Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468
Citation: Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468

基于半正定约束的极化相似度最优模型匹配目标分解

doi: 10.11999/JEIT141468
基金项目: 

国家自然科学基金(61271024, 61201292, 61201283),新世纪优秀人才支持计划(NCET-09-0630),全国优秀博士学位论文作者专项资金(FANEDD-201156),省部级基金和中央高校基本科研业务费

Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity

  • 摘要: 目标分解是实现极化合成孔径雷达目标分类、检测与识别应用的重要手段。传统方法由于优先对体散射分量进行提取,其体散射能量的高估或二面角散射能量的低估现象较为严重。该文通过引入极化相似度量,基于数据驱动自适应地对基本散射机制的最优匹配模型进行选择。在此基础上,根据极化相似度量确定基本散射机制散射能量提取的优先顺序,并以各阶次剩余矩阵能量非负为约束,最终确定面散射、二面角散射、体散射这3种基本散射机制的能量贡献值。实测数据处理结果及其与光学图像的对比结果表明,该文方法获取的极化目标分解结果优于传统方法,能够准确地提取目标区域的基本散射特征。
  • Boerner W M, Yan W L, Xi A Q, et al.. Basic Concepts of Radar Polarimetry[M]. Netherlands: Springer, 1992: 155-245.
    Boerner W M. Basics of SAR Polarimetry I[R]. Chicago, IL: 2007.
    Mott H. Remote Sensing with Polarimetric Radar[M]. New York: Wiley-IEEE Press, 2007: 3-19.
    Cloude S R. Polarisation: Applications in Remote Sensing[M]. Oxford: Oxford University Press, 2009: 4-103.
    Lee J S and Pottier E. Polarimetric Radar Imaging From Basics to Applications[M]. United States: CRC Press, 2009: 5-53.
    Zebker H A and van Zyl J J. Imaging radar polarimetry: a review[J]. Proceedings of the IEEE, 1991, 79(11): 1583-1606.
    Chen Q, Kuang G Y, Li J, et al.. Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1817-1825.
    Frery A C, Cintra R J, and Nascimento A. Entropy-based statistical analysis of PolSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3733-3743.
    Kajimoto M and Susaki J. Urban-area extraction from polarimetric SAR images using polarization orientation angle[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2): 337-341.
    Zhang P, Li M, Wu Y, et al.. Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model[J]. Pattern Recognition, 2013, 46(4): 1-16.
    Ballester-Berman J D and Lopez-Sanchez J M. Applying the Freeman-Durden decomposition concept to polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(1): 466-479.
    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.
    Yamaguchi Y, Sato A, Sato R, et al.. Four-component scattering power decomposition with rotation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2251-2258.
    Yamada H, Komaya R, Yamaguchi Y, et al.. Scattering component decomposition for POL-InSAR dataset and its applications[C]. Geoscience and Remote Sensing Symposium, Cape Town, 2009: V-154-V-157.
    Van Zyl J J, Arii M, and Kim Y. Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3452-3459.
    Cloude S R and Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
    Singh G, Yamaguchi Y, and Park S E. General four- component scattering power decomposition with unitary transformation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 3014-3022.
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出版历程
  • 收稿日期:  2014-11-24
  • 修回日期:  2015-04-24
  • 刊出日期:  2015-08-19

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