极化SAR图像分类方法研究
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摘要
近年来,极化合成孔径雷达(Synthetic Aperture Radar,SAR)已成为遥感领域最先进的传感器之一。极化SAR图像分类是极化SAR图像解译的重要研究内容,在民用和军用领域均有着巨大的应用价值和理论意义。主要着眼于分类精度的提高和散射类型的准确描述,本文对极化SAR图像分类相关技术进行了系统深入的研究。开展的工作主要包括以下几个方面:
(1)雷达极化测量基础理论的深入分析。为进一步消除极化界在极化测量基本方程理解上产生的一些混乱和模糊,利用方向性Jones矢量和时间反演算子,给出了场方程和电压方程下Sinclair散射矩阵极化基变换的一种合理推导。通过正向和反向传播空间概念的引入,从理论上说明了Mueller矩阵和Kennaugh矩阵的本质一致性。此外,还对部分极化波进行了较深入的分析。
(2)极化SAR测量数据的统计建模。基于斑点乘积模型,导出了散射矢量的五个新分布( KP分布、GP 0分布、GP H分布、GP 1分布和GP 2分布)和极化协方差矩阵的两个新分布( GP 1分布和GP 2分布)。在现有分布中, GP 2分布最适合同时对均匀区域、一般不均匀区域和极不均匀区域的数据进行描述。推导了GP 1分布和GP 2分布参数的矩估计式,着重提出了参数估计的最优化方法。与矩估计法相比,最优化方法的估计误差更小,稳健性更高。
(3)极化SAR图像的有监督统计分类。提出了一个基于最大后验概率准则(MAP)、GP 2分布和马尔可夫随机场(MRF)的迭代分类方法(GMMAP方法)。该方法在理论上可获得最小分类错误率,并可解决分类过程中小容量训练样本难以对统计模型的参数进行准确估计的问题。
(4)极化SAR目标的散射随机性度量。提出了一个可以反映目标散射随机性随入射波极化态变化的新度量——随机度。定义了“随机度特征图”对随机度进行可视化描述。给出了随机度均值和标准差的定义式,并着重分析了“水平-垂直”线极化波、“45°-135°”线极化波以及“左旋-右旋”圆极化波激励所得的平均随机度。该参数与散射熵的变化规律几乎一样,且二者之间存在一个近似关系,但其计算仅涉及一些简单操作,不需进行特征值分解,速度要比散射熵的计算快得多,因此在实际工程应用中,可考虑用平均随机度代替散射熵。
(5)极化SAR图像的无监督散射分类。提出了一个基于目标主散射机制和散射随机性度量的极化SAR图像散射分类框架。在该框架下,针对H /α分类存在的问题,提出了一个基于特征分解、Krogager分解和散射熵的新方法(EKE方法)。为实现非相干情况下主散射机制的直接提取和鉴别,提出了一个基于Freeman分解和平均随机度的分类方法(FDR方法)。为了进一步改善分类效果,结合Wishart距离度量,提出了EKE-Wishart迭代分类方法和FDR-Wishart迭代分类方法。所提方法的有效性得到了实测数据的实验验证。
Polarimetric synthetic aperture radar (SAR) has become one of the most advanced remote sensors in recent years. As one of the main tasks for understanding polarimetric SAR images, polarimetric SAR image classification has been playing an important role in many fields of both civil and military applications. To improve classification accuracy and reveal target scattering mechanisms, some key techniques concerned with polarimetric SAR image classification are investigated in this dissertation.
1) Deep analysis of some fundamental theories of radar polarimetry. To further clear some misunderstandings and inconsistencies of some concepts related to the basic polarimetric equations, an exact derivation of basis transformation of the Sinclair scattering matrix is given using the directional Stokes vector and the time reversal operator, which are introduced by Graves and Luneburg, respectively. The consistency in essence of the Mueller matrix and the Kennaugh matrix is explained in theory by introducing the concepts of positive and opposite propagation spaces. In addition, partially polarized waves are also deep analyzed.
2) Statistical modeling of polarimetric SAR data. Based on the multiplicative speckle model, five new statistical distributions ( KP distribution, GP 0 distribution, GP H distribution, GP 1 distribution and GP 2 distribution) for the scattering vector and two new statistical distributions ( GP 1 distribution and GP 2 distribution) for the polarimetric covariance matrix are proposed. The GP 2 distribution is most appropriate to model homogeneous, heterogeneous and extremely heterogeneous clutter. The estimators using moments for the roughness parameters of the GP 1 and GP 2 distributions are given. To obtain more accurate and robust estimation, an optimization method is proposed.
3) Supervised statistical classification of polarimetric SAR images. An iterative classification method of polarimetric SAR images (GMMAP method), based on the maximum a posteriori (MAP) criterion, the GP 2 distribution and the Markov random field (MRF), is proposed. The method can achieve least classification error in theory. And because of the introduction of new samples through iterations, the method solves the problem that class statistics are probably not estimated accurately with a limited training sample set.
4) Scattering randomness measurement of polarimetric SAR targets. In order to reflect the variation of target scattering randomness with the polarization sates of incident waves, a novel measure of target scattering randomness, which is called Degree of Randomness (DoR), is proposed. The concept of DoR Signature is introduced for the visualized description of the DoR. The mean and standard deviation of the DoR is defined. The mean of the DoR for“horizontal-vertical”linear,“45°-135°”linear and“left-right”circular polarization waves is analyzed. The variation of this parameter is almost the same as that of the scattering entropy and there exists an approximation relationship between them. And the computation of the parameter is simpler and faster than that of the scattering entropy.
5) Unsupervised scattering classification of polarimetric SAR images. First, based on the dominant scattering mechanism and the scattering randomness measure, a scattering classification frame is constructed. Then, to solve the problems existing in the H /αclassification, a new method (EKE method), based on the eigen decomposition, Krogager decomposition and scattering entropy, is proposed. To directly extract and discriminate the dominant scattering mechanism in the incoherent case, another new method (FDR method), based on the Freeman decomposition and the mean DoR, is proposed. Finally, to further improve the classification performance, combining the EKE and FDR methods with the Wishart distance measure, two iterative classification methods (EKE-Wishart and FDR-Wishart) are developed.
(1)雷达极化测量基础理论的深入分析。为进一步消除极化界在极化测量基本方程理解上产生的一些混乱和模糊,利用方向性Jones矢量和时间反演算子,给出了场方程和电压方程下Sinclair散射矩阵极化基变换的一种合理推导。通过正向和反向传播空间概念的引入,从理论上说明了Mueller矩阵和Kennaugh矩阵的本质一致性。此外,还对部分极化波进行了较深入的分析。
(2)极化SAR测量数据的统计建模。基于斑点乘积模型,导出了散射矢量的五个新分布( KP分布、GP 0分布、GP H分布、GP 1分布和GP 2分布)和极化协方差矩阵的两个新分布( GP 1分布和GP 2分布)。在现有分布中, GP 2分布最适合同时对均匀区域、一般不均匀区域和极不均匀区域的数据进行描述。推导了GP 1分布和GP 2分布参数的矩估计式,着重提出了参数估计的最优化方法。与矩估计法相比,最优化方法的估计误差更小,稳健性更高。
(3)极化SAR图像的有监督统计分类。提出了一个基于最大后验概率准则(MAP)、GP 2分布和马尔可夫随机场(MRF)的迭代分类方法(GMMAP方法)。该方法在理论上可获得最小分类错误率,并可解决分类过程中小容量训练样本难以对统计模型的参数进行准确估计的问题。
(4)极化SAR目标的散射随机性度量。提出了一个可以反映目标散射随机性随入射波极化态变化的新度量——随机度。定义了“随机度特征图”对随机度进行可视化描述。给出了随机度均值和标准差的定义式,并着重分析了“水平-垂直”线极化波、“45°-135°”线极化波以及“左旋-右旋”圆极化波激励所得的平均随机度。该参数与散射熵的变化规律几乎一样,且二者之间存在一个近似关系,但其计算仅涉及一些简单操作,不需进行特征值分解,速度要比散射熵的计算快得多,因此在实际工程应用中,可考虑用平均随机度代替散射熵。
(5)极化SAR图像的无监督散射分类。提出了一个基于目标主散射机制和散射随机性度量的极化SAR图像散射分类框架。在该框架下,针对H /α分类存在的问题,提出了一个基于特征分解、Krogager分解和散射熵的新方法(EKE方法)。为实现非相干情况下主散射机制的直接提取和鉴别,提出了一个基于Freeman分解和平均随机度的分类方法(FDR方法)。为了进一步改善分类效果,结合Wishart距离度量,提出了EKE-Wishart迭代分类方法和FDR-Wishart迭代分类方法。所提方法的有效性得到了实测数据的实验验证。
Polarimetric synthetic aperture radar (SAR) has become one of the most advanced remote sensors in recent years. As one of the main tasks for understanding polarimetric SAR images, polarimetric SAR image classification has been playing an important role in many fields of both civil and military applications. To improve classification accuracy and reveal target scattering mechanisms, some key techniques concerned with polarimetric SAR image classification are investigated in this dissertation.
1) Deep analysis of some fundamental theories of radar polarimetry. To further clear some misunderstandings and inconsistencies of some concepts related to the basic polarimetric equations, an exact derivation of basis transformation of the Sinclair scattering matrix is given using the directional Stokes vector and the time reversal operator, which are introduced by Graves and Luneburg, respectively. The consistency in essence of the Mueller matrix and the Kennaugh matrix is explained in theory by introducing the concepts of positive and opposite propagation spaces. In addition, partially polarized waves are also deep analyzed.
2) Statistical modeling of polarimetric SAR data. Based on the multiplicative speckle model, five new statistical distributions ( KP distribution, GP 0 distribution, GP H distribution, GP 1 distribution and GP 2 distribution) for the scattering vector and two new statistical distributions ( GP 1 distribution and GP 2 distribution) for the polarimetric covariance matrix are proposed. The GP 2 distribution is most appropriate to model homogeneous, heterogeneous and extremely heterogeneous clutter. The estimators using moments for the roughness parameters of the GP 1 and GP 2 distributions are given. To obtain more accurate and robust estimation, an optimization method is proposed.
3) Supervised statistical classification of polarimetric SAR images. An iterative classification method of polarimetric SAR images (GMMAP method), based on the maximum a posteriori (MAP) criterion, the GP 2 distribution and the Markov random field (MRF), is proposed. The method can achieve least classification error in theory. And because of the introduction of new samples through iterations, the method solves the problem that class statistics are probably not estimated accurately with a limited training sample set.
4) Scattering randomness measurement of polarimetric SAR targets. In order to reflect the variation of target scattering randomness with the polarization sates of incident waves, a novel measure of target scattering randomness, which is called Degree of Randomness (DoR), is proposed. The concept of DoR Signature is introduced for the visualized description of the DoR. The mean and standard deviation of the DoR is defined. The mean of the DoR for“horizontal-vertical”linear,“45°-135°”linear and“left-right”circular polarization waves is analyzed. The variation of this parameter is almost the same as that of the scattering entropy and there exists an approximation relationship between them. And the computation of the parameter is simpler and faster than that of the scattering entropy.
5) Unsupervised scattering classification of polarimetric SAR images. First, based on the dominant scattering mechanism and the scattering randomness measure, a scattering classification frame is constructed. Then, to solve the problems existing in the H /αclassification, a new method (EKE method), based on the eigen decomposition, Krogager decomposition and scattering entropy, is proposed. To directly extract and discriminate the dominant scattering mechanism in the incoherent case, another new method (FDR method), based on the Freeman decomposition and the mean DoR, is proposed. Finally, to further improve the classification performance, combining the EKE and FDR methods with the Wishart distance measure, two iterative classification methods (EKE-Wishart and FDR-Wishart) are developed.
引文
[1]张澄波.综合孔径雷达——原理、系统分析与应用[M].北京:科学出版社, 1989.
[2]张直中.微波成像技术[M].北京:科学出版社, 1990.
[3]保铮等.雷达成像技术[M].北京:电子工业出版社, 2003.
[4]刘永坦.雷达成像技术[M].哈尔滨:哈尔滨工业大学出版社, 1999.
[5] Young S H H. Gallery of USAF weapons: 2007 USAF almanac[J]. Air Force Magazine, May, 2007: 135-158.
[6] van Zyl J J, Zebker H A, Elachi C. Imaging radar polarization signatures: theory and observation[J]. Radio Science, 1987, 22(4): 529-543.
[7] Zebker H A, van Zyl J J. Imaging radar polarimetry: a review[J]. Proceedings of the IEEE, 1991, 79(11): 1583-1606.
[8] Touzi R, et al. A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction[J]. Canadian Journal of Remote Sensing, 2004, 30(3): 380-407.
[9] Lee J S, et al. A review of polarimetric SAR algorithms and their applications[J]. Taiwan Journal of Photogrammetry and Remote Sensing, 2004, 9(3): 31-80.
[10]庄钊文,肖顺平,王雪松.雷达极化信息处理及其应用[M].北京:国防工业出版社, 1999.
[11] Kennaugh E M. Polarization properties of radar reflections[D]. Columbus, OH, USA: The Ohio State University, 1952.
[12] van Zyl J J. On the importance of polarization in radar scattering problems[D]. Pasadena, CA, USA: California Institute of Technology, 1985.
[13] Agrawal A P. A polarimetric rain backscatter model developed for coherent polarization diversity radar applications[D]. Chicago, IL, USA: University of Illinois, 1986.
[14] Yang J. On theoretical problems in radar polarimetry[D]. Niigata-shi, Japan: Niigata University, 1999.
[15]苏瑞龙.基因演绎法于全偏极合成孔径雷达影像对比强化最优化之研究[D].台湾:国立中央大学, 2003.
[16] Agrawal A P, Boerner W-M. Redevelopment of Kennaugh’s target characteristic polarization state theory using the polarization transformation ration formalism for the coherent case[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 2-14.
[17] Boerner W-M, et al. On the basic principles of radar polarimetry: the targetcharacteristic polarization state theory of Kennaugh, Huynen’s polarization fork concept, and its extension to the partially polarized case[J]. Proceedings of the IEEE, 1991, 79(10): 1538-1550.
[18] Huynen J R. Phenomenological theory of radar targets[D]. Delft, The Netherlands: Technical University of Delft, 1970.
[19] Cloude S R, Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
[20] Cameron W L, Youssef N N, Leung L K. Simulated polarimetric signatures of primitive geometrical shapes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 793-803.
[21] Cloude S R, Pottier E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 549-557.
[22] Freeman A, 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.
[23] Dong Y, Forster B C, Ticehurst C. A new decomposition of radar polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 933-939.
[24] Touzi R, Charbonneau F. Characterization of target symmetric scattering using polarimetric SARs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2507-2516.
[25] Yamaguchi Y, et al. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699-2005.
[26] Xu F, Jin Y Q. 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.
[27] Cameron W L, Rais H. Conservative polarimetric scatterers and their role in incorrect extensions of the Cameron decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12): 3506-3516.
[28] Yamaguchi Y, Yajima Y, Yamada H. A four-component decomposition of POLSAR images based on the coherency matrix[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(3): 292-296.
[29] Touzi R. Target scattering decomposition in terms of roll-invariant target parameters[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1): 73-84.
[30] Lee J S, et al. Polarization orientation estimation and applications: a review[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 428-430.
[31] Pottier E, et al. Estimation of the terrain surface azimuthal/range slopes using polarimetric decomposition of PolSAR Data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999:2212-2214.
[32] Lee J S, Schuler D L, Ainsworth T L. Polarimetric SAR data compensation for terrain azimuth slope variation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2153-2163.
[33] Lee J S, et al. On the estimation of radar polarization orientation shifts induced by terrain slopes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(1): 30-41.
[34] Schuler D L, Lee J S, De Grandi G. Measurement of topography using polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(5): 1266-1277.
[35] Schuler D L, et al. Terrain topography measurement using multipass polarimetric synthetic aperture radar data[J]. Radio Science, 2000, 35(3): 813-832.
[36] Novak L M, Burl M C. Optimal speckle reduction in polarimetric SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(2): 293-305.
[37] Lee J S, Grunes M R, Mango S A. Speckle reduction in multipolarization, multifrequency SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1991, 29(4): 535-544.
[38] Goze S, Lopes A. A MMSE speckle filter for full resolution SAR polarimetric data[J]. Journal of Electromagnetic Waves and Applications, 1993, 7(5): 717-737.
[39] Touzi R, Lopes A. The principle of speckle filtering in polarimetric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1110-1114.
[40] Fleischman J G, et al. Multichannel whitening of SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(1): 156-166.
[41] Lopes A, Sery F. Optimal speckle reduction for the product model in multilook polarimetric SAR imagery and the Wishart distribution[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 632-647.
[42] Liu G, et al. The multilook polarimetric whitening filter (MPWF) for intensity speckle reduction in polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 1016-1020.
[43] Lee J S, Grunes M R, De Grandi G. Polarimetric SAR speckle filtering and itsimplication for classfication[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2363-2373.
[44] Schou J, Skriver H. Restoration of polarimetric SAR images using simulated annealing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(9): 2005-2016.
[45] Touzi R. A review of speckle filtering in the context of estimation theory[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2392-2404.
[46] Lopez-Martinez C, Fabregas X. Polarimetric SAR speckle noise model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2232-2242.
[47] Gu J, et al. Speckle filtering in polarimetric SAR data based on the subspace decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1635-1641.
[48] Lee J S, et al. Scattering-model-based speckle filtering of polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(1): 176-187.
[49] Lopez-Martinez C. Multidimensional speckle noise, modelling and filtering related to SAR data[D]. Barcelona, Spain: Technical University of Catalonia, 2003.
[50] Novak L M, Burl M C. Studies of target detection algorithms that use polarimetric radar data[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 150-165.
[51] Chaney R D, Burl M C, Novak L M. On the performance of polarimetric target detection algorithms[J]. IEEE Transactions on Aerospace and Electronic Systems Magazine, 1990, 5(11): 10-15.
[52] Novak L M, Burl M C. Optimal polarimetric processing for enhanced target detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 234-244.
[53] Touzi R. On the use of polarimetric SAR data for ship detection[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 812-814.
[54] Touzi R. Calibrated polarimetric SAR data for ship detection[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’00)[C], Honolulu, HI, USA, 2000: 144-146.
[55] Conradsen K, et al. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1): 4-19.
[56] Schou J, et al. CFAR edge detector for polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1): 20-32.
[57] Touzi R, et al. Ship detection and characterization using polarimetric SAR[J]. Canadian Journal of Remote Sensing, 2004, 30(3): 552-559.
[58] Kong J A, et al. Identification of terrain cover using the optimal polarimetric classifier[J]. Journal of Electromagnetic Waves and Applications, 1988, 2(2): 171-194.
[59] Yueh H A, et al. Bayes classification of terrain cover using normalized polarimetric data[J]. Journal of Geophysical Research, 1988, 93(B12): 15261-15267.
[60] Lim H H, et al. Classification of earth terrain using polarimetric synthetic aperture radar images[J]. Journal of Geophysical Research, 1989, 94(B6): 7049-7057.
[61] van Zyl J J, Burnette C F. Bayesian classification of polarimetric SAR images using adaptive a priori probability[J]. International Journal of Remote Sensing, 1992, 13(5): 835-840.
[62] Lee 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.
[63] Chen K S, et al. Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 814-820.
[64] Benz U C. Supervised fuzzy analysis of single- and multichannel SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 1023-1037.
[65] Fukuda S, Hirosawa H. A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2282-2286.
[66] Keshava N, Moura J M F. Matching wavelet packets to Gaussian random processes[J]. IEEE Transactions on Signal Processing, 1999, 47(6): 1604-1614.
[67] Lee J S, Grunes M R, Pottier E. Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2343-2351.
[68] Chen C T, Chen K S, Lee J S. The use of fully polarimetric information for the fuzzy neural classification of SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2089-2100.
[69] Pellizzeri T M, et al. Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2338-2353.
[70] Lombardo P, et al. Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas[J]. IEEE Transactions onGeoscience and Remote Sensing, 2003, 41(9): 1959-1975.
[71] Kouskoulas Y, Ulaby F T, Pierce L E. The Bayesian hierarchical classifier (BHC) and its application to short vegetation using multifrequency polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(2): 469-477.
[72] van Zyl J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36-45.
[73] Rignot E, Chellappa R, Dubois P. Unsupervised segmentation of polarimetric SAR data using the covariance matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 697-705.
[74] Rignot E, Chellappa R. Segmentation of polarimetric synthetic aperture radar data[J]. IEEE Transactions on Image Processing, 1992, 1(3): 281-300.
[75] Wong Y F, Posner E C. A new clustering algorithm applicable to multispectral and polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(3): 634-644.
[76] Pierce L E., et al. Knowledge-based classification of polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1081-1086.
[77] Hara Y, et al. Application of neural networks to radar image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(1): 100-109.
[78] Lee J S, 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.
[79] Ferro-Famil L, Pottier E, Lee J S. Unsupervised classfication of multifrequency and fully polarimetric SAR images based on the H/A/Alpha - Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2332-2342.
[80] Dong Y, Milne A K, Forster B C. Segmentation and classification of vegetated areas using polarimetric SAR image data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(2): 321-329.
[81] Lee J S, et al. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722-731.
[82] Kersten P R, Lee J S, Ainsworth T L. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 519-527.
[83] Hoekman D H, Quinones M J. Land cover type and biomass classification using AirSAR data for evaluation of monitoring scenarios in the Colombian Amazon[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(2): 685-696.
[84] Trizna D B, et al. Projection pursuit classification of multiband polarimetric SARland images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2380-2386.
[85] Hoekman D H, Quinones M J. Biophysical forest type characterization in the Colombian Amazon by airborne polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(6): 1288-1300.
[86] De Grandi G, et al. Texture and speckle statistics in polarimetric SAR synthesized images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2070-2088.
[87] Beaulieu J-M, Touzi R. Segmentation of textured polarimetric SAR scenes by likelihood approximation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2063-2072.
[88] Barnes C F, Burki J. Late-season rural land-cover estimation with polarimetric-SAR intensity pixel blocks andσ-tree-structured near-neighbor classifiers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9): 2384-2392.
[89] Alberga V. Comparison of polarimetric methods in image classification and SAR interferometry applications[D]. Chemnitz, Saxony, Germany: Technical University of Chemnitz, 2004.
[90] Kimura K. A study on target classification/detection in polarimetric SAR image data[D]. Niigata-shi, Japan: Niigata University, 2005.
[91] Dubois P C, van Zyl J J, Engman T. Measuring soil moisture with imaging radars[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4): 915-926.
[92] Mattia F, et al. The effect of surface roughness on multifrequency polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 954-966.
[93] Hajnsek I, Pottier E, Cloude S R. Inversion of surface parameters from polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 727-744.
[94] Hajnsek I. Inversion of surface parameters using polarimetric SAR[D]. Jena, Germany: Friedrich-Schiller University Jena, 2001.
[95] Lopez-Sanchez J M. Analysis and estimation of biophysical parameters of vegetation by radar polarimetry[D]. Valencia, Spain: Universidad Politecnica de Valencia, 1999.
[96] Cloude S R, Papathanassiou K P. Polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1551-1565.
[97] Papathanassiou K P, Cloude S R. Single-baseline polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(11): 2352-2363.
[98]郭华东等.极化干涉雷达遥感机制及应用[J].遥感学报, 2002, 6(6): 401-405.
[99]李新武.极化干涉SAR信息提取方法及其应用研究[D].北京:中国科学院, 2002.
[100] Reigber A, Moreira A. First demonstration of Airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2142-2152.
[101] Reigber A. Airborne polarimetric SAR tomography[D]. Stuttgart, Germany: University of Stuttgart, 2001.
[102] Lombardini F, Reigber A. Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 2014-2016.
[103]郭华东.雷达对地观测理论与应用[M].北京:科学出版社, 2000.
[104]谭衢霖,胡吉平.成像雷达遥感的生态学应用[J].遥感技术与应用, 2005, 20(3): 375-380.
[105]谭衢霖,邵芸.成像雷达(SAR)遥感地质应用综述[J].地质找矿论丛, 2003, 18(1): 59-65.
[106] ESA. Input data sources: airborne missions[EB/OL]. http://earth.esa.int/polsarpro/input.html, 2006-12-20.
[107] van Zyl J, et al. The NASA/JPL three-frequency polarimetric AirSAR system[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’92)[C], Houston, TX, USA, 1992: 649-651.
[108] Chu A, et al. The NASA/JPL AirSAR integrated processor[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’98)[C], Washington, USA, 1998: 1908-1910.
[109] Livingstone C E, et al. CCRS/DREO synthetic aperture radar polarimetry - status report[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’90)[C], Maryland, USA, 1990: 1671-1674.
[110] Livingstone C E, et al. The Canadian airborne R&D SAR facility: the CCRS C/X SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’96)[C], Nebraska, USA, 1996: 1621-1623.
[111] Horn R, Werner M, Mayr B. Extension of the DLR airborne synthetic aperture radar, E-SAR, to X-band[a]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’90)[C], Maryland, USA, 1990: 2047-2049.
[112] Horn R. The DLR airborne SAR project E-SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’96)[C], Nebraska, USA, 1996: 1624-1628.
[113] Scheiber R, et al. Overview of interferometric data acquisition and processing modes of the experimental airborne SAR system of DLR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 35-37.
[114] Skou N, et al. A high resolution polarimetric L-band SAR - design and first results[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 1779-1782.
[115] Christensen E L, et al. EMISAR: an absolutely calibrated polarimetric L- and C-band SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(6): 1852-1865.
[116] Christensen E L, Dall J. EMISAR: a dual-frequency, polarimetric airborne SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 1711-1713.
[117] Uratsuka S, et al. High-resolution dual-bands interferometric and polarimetric airborne SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002:1720-1722.
[118] Uratsuka S, et al. Disastrous environment after earthquake observed by airborne SAR (Pi-SAR)[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 4081-4083.
[119] ESA. Input data sources: spacaeborne missions[EB/OL]. http://earth.esa.int/polsarpro/input_space.html, 2006-12-20.
[120] Jordan R L, Huneycutt R L, Werner M. The SIR-C/X-SAR synthetic aperture radar system[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4): 829-839.
[121] Vant M, Livingstone C, Rey M. Canadian experience on Radarsat-1 and Radarsat-2 GMTI for surveiliance[A]. In: Proc. AIAA/ICAS International Air and Space Symposium and Exposition: The Next 100 Year[C], Dayton, OH, USA , 2003:1-10.
[122] van der Sanden J J, Thomas S J. Applications potential of Radarsat-2-supplement one[R]. Ottawa, Canada: Natural Resources Canada, Canada Centre for Remote Sensing, 2004.
[123] Lee J S, et al. Statistical analysis and segmentation of multi-look SAR imagery using partial polarimetric data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95), Chengdu, China, 1995:1422-1424.
[124] Lee J S, et al. Quantitative comparison of classification capability: fully-polarimetric versus partially polarimetric SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’00)[C], Honolulu, HI, USA, 2000:1101-1103.
[125] Ainsworth T L, Lee J S. Optimal image classification employing“optimal”polarimetric variables[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003:696-698.
[126] Ainsworth T. L, Lee J S. Polarimetric SAR image classification employing subaperture polarimetric analysis[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005:48-50.
[127] van Zyl J J. Application of Cloude’s target decomposition theorem to polarimetric imaging radar data[A]. In: Proc. SPIE Vol. 1748 Radar Polarimetry[C], San Diego, CA, USA, 1992: 184-212.
[128] Burl M C, Novak L M. Polarimetric segmentation of SAR imagery[A]. In: Proc. SPIE Vol. 1471 Automatic Object Recognition[C], Orlando, FL, USA, 1991: 92-115.
[129] Pottier E, Saillard J. On radar polarization target decomposition theorems with application to target classification by Using Network Method[A]. In: Proc. ICAP’91[C], York, England, 1991: 265-268.
[130] Pottier E. Classification of earth terrain in polarimetric SAR images using neural nets modelization[A]. In: Proc. SPIE Vol. 1748 Radar Polarimetry[C], San Diego, CA, USA, 1992: 321-332.
[131] Pottier E. Radar target decomposition theorems and unsupervised classification of full polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994: 1139-1141.
[132] Pottier E, Cloude S R. Unsupervised classification of full polarimetric SAR data and feature vectors identification using radar target decomposition theorems and entropy analysis[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 2247-2249.
[133] Pottier E, Cloude S R. Application of the H/A/αpolarimetric decomposition theorems for land classification[A]. In: Proc. SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry[C], San Diego, CA, USA, 1997: 132-143.
[134] Hellmann M, Jager G, Pottier E. Fuzzy clustering and interpretation of fully polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’01), Sydney, Australia, 2001: 2790-2792.
[135] Cloude S R. An entropy based classification scheme for polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995:2000-2002.
[136] Lombardo P. Optimal classification of polarimetric SAR images using segmentation[A]. In: Proc. IEEE on Radar Conference[C], Long Beach, CA, USA, 2002: 8-13.
[137] Pellizzeri T M, Lombardo P, Ferriero P. Polarimetric SAR image processing: Wishart vs“H/A/alpha”segmentation and classification schemes[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 3976-3978.
[138] Hellmann M, et al. Classification of full polarimetric SAR-data using artificial neural networks and fuzzy Algorithms[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 1995-1997.
[139] Alberga V, Satalino G, Staykova D K. Polarimetric SAR observables for land cover classification: analyses and comparisons[A]. In: Proc. SPIE Vol. 6363 SAR Image Analysis, Modeling, and Techniques VIII[C], Stockholm, Sweden, 2006: 636305.
[140] Fukuda S, Suwa K, Hirosawa H. Texture and statistical distribution in high resolution polarimetric SAR images[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 1268-1270.
[141] Fukuda S, Hirosawa H. Support vector machine classification of land cover: application to polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’01)[C], Sydney, Australia, 2001: 187-189.
[142] Fukuda S, Katagiri R, Hirosawa H. Unsupervised approach for polarimetric SAR image classification using support vector machines[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 2599-2601.
[143] Xu J Y, Yang J, Peng Y N. New method of feature extraction in polarimetric SAR image classification[A]. In: Proc. SPIE Vol. 4741 Battlespace Digitization and Network-Centric Warfare II[C], Orlando, FL, USA, 2002: 337-344.
[144] Xu J Y, et al. Using cross-entropy for polarimetric SAR image classification[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 1917-1919.
[145]徐俊毅,杨健,彭应宁.双波段极化雷达遥感图像分类的新方法[J].中国科学(E辑), 2005, 35(10): 1083-1095.
[146]徐丰,金亚秋.目标散射的去取向理论和应用(一)去取向分析[J].电波科学学报, 2006, 21(1): 6-15.
[147]徐丰,金亚秋.目标散射的去取向理论和应用(二)地表分类应用[J].电波科学学报, 2006, 21(2): 153-160.
[148] Jin Y Q, Chen F. Polarimetric scattering indexes and information entropy of theSAR imagery for surface monitoring[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2502-2506.
[149] Jn Y Q, Xu F. A new set of the parameters for the terrain surface classification in polarimetric SAR image based on deorientation of polarimetric scattering vector[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’06), Denver, CO, USA, 2006: 1403-1406.
[150]金亚秋,陈扉. SAR图像中极化散射指数和信息熵及其地表识别应用[J].自然科学进展, 2003, 13(2): 174-178.
[151]王之禹,朱敏慧,白有天.基于Mueller矩阵分解的非监督聚类算法[J].电子与信息学报, 2001, 23(5): 454-459.
[152]刘秀清,杨汝良.基于全极化SAR非监督分类的迭代分类方法[J].电子学报, 2004, 32(12): 1982-1986.
[153]刘秀清,杨汝良,杨震.双波段全极化SAR图像非监督分类方法及实验研究[J].电子与信息学报, 2004, 26(11): 1738-1745.
[154]王之禹,朱敏慧,白有天.基于最优状态的多波段全极化SAR数据ML分类方法[J].电子与信息学报, 2001, 23(5): 507-511.
[155]李晓玮,种劲松.基于目标相干散射特性的极化SAR图像分解分类方法[J].遥感技术与应用, 2007, 22(3): 443-448.
[156]戴博伟.多极化合成孔径雷达系统与极化信息处理研究[D].北京:中国科学院, 2000.
[157]刘秀清.全极化合成孔径雷达极化信息处理技术研究[D].北京:中国科学院, 2004.
[158]王之禹.全极化合成孔径雷达在地物分类领域的应用研究[D].北京:中国科学院, 2001.
[159]王翠珍.极化SAR数据分析与目标信息提取[D].北京:中国科学院, 1999.
[160] Tzeng Y C, et al. A dynamic learning neural network for remote sensing applications[J]. IEEE Transactions On Geoscience And Remote Sensing, 1994, 32(5): 1096-1102.
[161]陈家堂.全偏极合成孔径雷达于目标分类之研究[D].台湾:国立中央大学, 2002.
[162]林佳驹.以特征为基础的模糊演算算法进行多波段-多极化NASA/JPL POLSAR影像分类之研究[D].台湾:国立成功大学, 2003.
[163]李一麟.空载全偏极POLSAR目标分类之DLBP演算法[D].台湾:国立成功大学, 2004.
[164] Liu G, et al. Bayesian classification of multi-look polarimetric SAR images witha generalized multiplicative speckle model[A]. In: Proc. SPIE Vol. 3070 algorithms for synthetic aperture radar imagery IV[C], Orlando, FL, USA, 1997: 398-405.
[165]刘国庆等.多视极化合成孔径雷达图象的分类和极化通道优化[J].电子科学学刊, 1998, 20(1): 56-61.
[166]刘国庆,熊红,黄顺吉.基于小波变换和马尔可夫随机场的极化SAR图像自动分类[J].电子科学学刊, 2000, 22(3): 359-365.
[167]谢艳.极化合成孔径雷达分类[D].成都:电子科技大学, 2006.
[168]吴永辉,计科峰,郁文贤.基于支持向量机的极化SAR图像分类[J].现代雷达, 2007, 29(6): 57-73.
[169]吴永辉,计科峰,郁文贤.基于H-α和改进C-均值的全极化SAR图像非监督分类[J].电子与信息学报, 2007, 29(1): 30-34.
[170]吴永辉.极化SAR图像分类技术研究[D].长沙:国防科技大学, 2007.
[171]代大海. POLSAR图像模拟及目标检测与分类方法研究[D].长沙:国防科技大学, 2003.
[172] Andreadis A, et al. Adaptive segmentation of multiband polarimetric SAR images for the identification of archaeological resources[A]. In: Proc. SPIE Vol. 2960 Remote Sensing for Geography, Geology, Land Planning, and Cultural Heritage [C], Taormina, Italy, 1996:148-152.
[173] Qong M, et al. Preliminary results from the model-based classification of airborne polarimetric SAR images[A]. In: Proc. SPIE Vol. 4152 Microwave Remote Sensing of the Atmosphere and Environment II[C], Sendai, Japan, 2000: 327-338.
[174] Kersten P R, Lee R R-Y. Segmentation polarimetric SAR images using robust competitive clustering[R]. Patuxent River, MD, USA: Naval Air Warfare Center Aircraft Division, 2001.
[175] Beaulieu J-M, Touzi R. Hierarchical segmentation of polarimetric SAR images[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02), Toronto, Canada, 2002: 2590-2592.
[176] Skriver H, et al. Polarimetric segmentation using Wishart test statistic. In: Proc. Internatonal Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 1011-1013.
[177] El Assad S, Saad A, Barba D. Unsupervised optimal fuzzy clustering and Markov segmentation of polarimetric radar imaging[A]. In: Proc. SPIE Vol. 2958 Microwave Sensing and Synthetic Aperture Radar[C], Taormina, Italy, 1996: 186-197.
[178] Borghys D, et al. Supervised feature-based classification of multi-channel SAR images using logistic regression[J]. 2000.
[179] Chen C H, Du Y. A multiresolution wavelet analysis for SAR image segmentation using statistical separability measures[A]. In: Proc. Part of the EUROPTO Conference on Image and Signal Processing for Remote Sensing[C], Barcelona, Spain, 1998: 104-110.
[180] Khan K U, Yang J. Novel features for polarimetric SAR images classification by neural network[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 165-170.
[181] Huynen J R. The Stokes matrix parameters and their interpretation in terms of physical target properties[A]. In: Proc. SPIE Vol. 1317 Polarimetry: Radar, Infrared, Visible, Ultraviolet, and X-Ray[C], Huntsville, AL, USA, 1990: 195-207.
[182] Pottier E. On Dr. J. R. Huynen’s main contributions in the development of the polarimetric radar techniques, and how the‘radar targets phenomenological concept’becomes a theory[A]. In: Proc. SPIE Radar Polarimetry[C], San Diego, CA, USA, 1992: 72-85.
[183] Huynen J R. Physical reality of radar target[A]. In: Proc. SPIE Vol. 1748 Radar Polarimetry[C], San Diego, CA, USA, 1992: 86-96.
[184] Huynen J R. A new extended target decomposition scheme[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994: 1124-1125.
[185] Yang J, et al. On Huynen's decomposition of a Kennaugh matrix[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(3): 369-372.
[186] Krogager E. New decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26: 1525-1527.
[187] Krogager E. Decomposition of the radar target scattering matrix with application to high resolution target imaging[A]. In: Proc. IEEE National Telesystems Conference (NTC’91)[C], Atlanta, GA, USA, 1991: 0077-0082.
[188] Krogager E. Wideband Pol-SAR/ISAR signal and image processing for target identification and discrimination[A]. In: Proc. IEEE Asia-Pacific Microwave Conference[C], Adelaide, Melbourne, Australia, 1992: 947-950.
[189] Krogager E. Aspects of polarimetric radar target imaging[D]. Lyngby, Denmark: Technical University of Denmark, 1993.
[190] Krogager E, Czyz Z H. Properties of the sphere, diplane, and helix decomposition[A]. In: Proc. Third International Workshop on Radar Polarimetry[C], IRESTE, University of Nantes, France, 1995: 106-114.
[191] Krogager E, Boerner W-M, Madsen S N. Feature-motivated Sinclair matrix (sphere/diplane/helix) decomposition and its application to target sorting for land feature classification[A]. In: Proc. SPIE Conference on Wideband InterferometricSensing and Imaging Polarimetry[C], San Diego, CA, USA, 1997: 144-154.
[192] Cameron W L, Leung L K. Feature motivated polarization scattering matrix decomposition[A]. In: Record of the IEEE 1990 International Radar Conference[C], Arlington, VA, USA, 1990: 549-557.
[193] Cloude S R. Radar target decomposition theorems[J]. Electronics Letters, 1985, 21(1): 22-24.
[194] Cloude S R. Target decomposition theorems applied to multiple scattering of polarised waves[A]. In: Proc. Interational Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994: 1126-1128.
[195] Praks J, et al. SAR target decomposition for stochastic natural targets for L- and C-band[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 1558-1560.
[196] Praks J, Hallikainen M. A novel approach in polarimetric cvariance matrix eigendecomposition[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’00)[C], Honolulu, HI, USA, 2000: 1119-1121.
[197] Ainsworth T L, Cloude S R, Lee J S. Eigenvector analysis of polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 626-628.
[198] Yang J, et al. New formula of the polarization entropy[J]. IEICE Transactions on Communication, 2006, E89-B(3): 1033-1035.
[199] Freeman A, Durden S. A three-component scattering model to describe polarimetric SAR data[A]. In: Proc. SPIE Conference on Radar Polarimetry[C], San Diego, CA, USA, 1992: 213-224.
[200] Moghaddam M, Freeman A. Modification to the three-component lassification algorithm for SAR data[A]. In: Proc. Progress in Electromagnetics Research Symposium[C], Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, 1993: 164.
[201] Freeman A. Fitting a two-component scattering model to polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99), Hamburg, Germany, 1999: 2649-2651.
[202] Qong M. Scattering mechanism identification based on the rotation and eccentric angles of polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’04)[C], Anchorage, AK, USA, 2004: 3054-3057.
[203] Aiazzi B, et al. Land cover classfication of built-up areas through enhanced fuzzy nearest-mean reclustering of textural features from X- and C-band polarimetric SAR data[A]. In: Proc. SPIE Vol. 5236, SAR Image Analysis, Modeling, and Techniques VI[C], Bellingham, WA, USA, 2004: 105-115.
[204] Lee J S, et al. K-distribution for multi-look processed polarimetric SAR imagery[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994:2179-2181.
[205] Beaulieu J-M, Touzi R. Segmentation of textured scenes using polarimetric SARs[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 446-448.
[206] Freitas C C, Frery A C, Correia A H. The polarimetric G distribution for SAR data analysis[J]. Environmetrics, 2005, 16(1): 13-31.
[207] Frery A, Correia A, Freitas C. Multifrequency full polarimetric SAR classification with multiple sources of statistical evidence[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’06)[C], Denver, CO, USA, 2006: 4195-4197.
[208] Park S-E, Moon W M. Classification of the polarimetric SAR using fuzzy boundaries in entropy and alpha plane[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 5517-5519.
[209] Putignano E, et al. Unsupervised classification of a central Italy landscape by polarimetric L-band SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 1291-1294.
[210] Pottier E, Lee J S, Ferro-Famil L. Advanced concepts in polarimetry-part 2 (Polarimetric Target Classification)[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
[211] VEXCEL. Earthview Matrix. http://www.vexcel.com, 2005.
[212] PCI Geomatics. Pcigeomatics ? SAR polarimetry workstation. http://www.ocigeomatics.com, 2008.
[213] Pottier E, et al. PolSARpro v2.0: the polarimetric SAR data processing and educational toolbox[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 3173-3176.
[214] Lee J S, et al. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1017-1028.
[215] Boerner W-M. Basics of SAR polarimetry I[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
[216] Boerner W-M. Basics of SAR polarimetry II[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
[217] Boerner W-M. Recent advances in extra-wide-band polarimetry, interferometry and polarimetric interferometry in synthetic aperture remote sensing, and its applications[J]. IEE Proceedings-Radar Sonar Navigation, Special Issue of the EUSAR-02, 2003, 150(3): 113-125.
[218] Boerner W-M. Recent advances in radar polarimetry and polarimetric SAR interferometry[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2004.
[219] Sinclair G. The transmission and reception of elliptically polarized waves[J]. Proceedings of the IRE, 1950, 38(2): 148-151.
[220] Deschamps G A. Geometrical representation of the polarization of a plane electromagnetic wave[J]. Proceedings of the IRE, 1951, 39(5): 540-544.
[221] Gent H. Elliptically polarized waves and their reflections from radar targets: a theoretical analysis[R]. Chelenham, England, UK: Telecommunications Research Establishment, 1954.
[222] Copeland J R. Radar target classification by polarization properties[J]. Proceedings of the IRE, 1960, 48(7): 1290-1296.
[223] Bickel S H. Some invariant properties of the polarization scattering matrix[J]. Proceedings of the IEEE, 1965, 53: 1970-1072.
[224] Boerner W-M, et al. Polarization dependence in electromagnetic inverse problems[J]. IEEE Transactions on Antennas and Propagation, 1981, AP-29(2): 262-271.
[225]王雪松.宽带极化信息处理的研究[D].长沙:国防科技大学, 1999.
[226]刘涛.瞬态极化统计理论及应用研究[D].长沙:国防科技大学, 2007.
[227]施龙飞.雷达极化抗干扰技术研究[D].长沙:国防科技大学, 2007.
[228]代大海.极化雷达成像及目标特征提取研究[D].长沙:国防科技大学, 2008.
[229] Guissard A. Mueller and Kennaugh matrices in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3): 590-597.
[230] Kostinski A B, Boerner W-M. On foundations of radar polarimetry[J]. IEEE Transactions on Antennas Propagation, 1986, AP-34(12): 1395-1404.
[231] Mieras H, Kostinski A B, Boerner W-M. Comments, with reply, on“on foundations of radar polarimetry”[J]. IEEE Transactions on Antennas Propagation, 1986, 34(12): 1470-1473.
[232] Hubbert J C. A comparison of radar, optic, and specular null polarization theories[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3): 658-671.
[233] Hubbert J C, Bringi V N. Specular null polarization theory: applications to radar meteorology[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 859-873.
[234] Luneburg E. Comments on“the specular null polarization theory”[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 1070-1071.
[235] Hubbert J C. Reply to“comments on‘the specular null polarization theory’”[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 1071-1072.
[236] Graves C S. Radar polarization power scattering matrix[J]. Proceedings of the IRE, 1956, 44(2): 248-252.
[237] Luneburg E, Boerner W-M. Kennaugh and the dual space approach to radar polarimetry[A]. In: Proc. IEEE Antennas and Propagation Society International Symposium[C], Columbus, OH, USA, 2003: 190-193.
[238] Luneburg E. Principles of radar polarimetry[J]. IEICE Transactions on Electronics (Special Issue on Electromagnetic Theory), 1995, E78-C(10): 1339-1345.
[239] Luneburg E. Directional Jones and Stokes vectors[R]. George Schmid Weg 4, Wessling, Germany: EML Consultants, 2005.
[240] Luneburg E, Cloude S R. Radar versus optical polarimetry[A]. In: Proc. SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry[C], San Diego, CA, USA, 1997: 361-372.
[241] Wigner E P. Normal form of antiunitary operators[J]. Journal of Mathematical Physics, 1960, 1(5): 409-413.
[242]陈军.光学位相共轭及其应用[M].北京:科学出版社, 1999.
[243] Luneburg E. Aspects of Radar Polarimetry[J]. Elektrik-Turkish Journal of Electrical Engineering & Computer Sciences (Special Issue on Electromagnetic Problems and Numerical Simulation Techniques Current Status - Future Trends), 2002, 10(2): 219-243.
[244] Keaton P W. Time reversal in polarization phenomena of nuclear interactions[R]. Los Alamos, NM, USA: Los Alamos Scientific Laboratory of the University of California, 1970.
[245] Meyer C D. Matrix analysis and applied linear algebra[M]. Philadelphia, PA, USA: Society for Industrial & Applied Mathematics (SIAM), 2004.
[246] Born M, Wolf E. Principles of optics: electromagnetic theory of propagation, interference and diffraction of light (7th Ed.)[M]. Cambridge, UK: Cambridge University Press, 1999.
[247] Gambini J, et al. Polarimetric SAR region boundary detection using B-spline deformable countours under the GH Model. In: Proc. XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI’05), Natal, RN, Brazil, 2005: 197-204.
[248] Gambini M J. Modelos De Segmentacion Basados En Regiones Y Contornos Activos Aplicados a Imagenes De Radar De Apertura Sintetica[D]. Buenos Aires, Argentina: Universidad de Buenos Aires, 2006.
[249] (英)丹蒂(Dainty J C)编,黄乐天等译.激光散斑及有关现象[M].北京:科学出版社, 1981.
[250] Goodman J W. Speckle phenomena in optics: theory and applications[M]. Englewood, CO, USA: Roberts & Company Publishers, 2006.
[251]刘培森.散斑统计光学基础[M].北京:科学出版社, 1986.
[252] (美)顾德门(Goodman J W)著,秦克诚等译.统计光学[M].北京:科学出版社, 1992.
[253] Frost V S, et al. A model for radar images and its application to adaptive digital filtering of multiplicative noise[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, PAMI-4(2): 157-165.
[254] Kuan D T, et al. Adaptive noise smoothing filter for imags with signal-dependent noise[J]. IEEE Transactions on Pattern Analysis and Machine Inteligence, 1985, PAMI-7(2): 165-177.
[255] Yoon S-H, Kim Y-S. Classified pixel-based windowing algorithm for polarimetric SAR speckle filtering[J]. Electronics Letters, 2003, 39(1): 115-116.
[256] OliverC, Quegan S. Understanding synthetic aperture radar imags[M]. Boston, MA: Artech House, 1998.
[257]贾承丽,匡纲要. SAR图像去斑方法[J].中国图象图形学报, 2005, 10(2): 135-141.
[258]王程. SAR图像相干抑制和光学图像序列超分辨技术研究[D].长沙:国防科技大学, 2002.
[259]付琨.高分辨率单视单极化SAR图像地物分类方法研究[D].长沙:国防科技大学, 2002
[260]高贵. SAR图像目标ROI自动获取技术研究[D].长沙:国防科技大学, 2007.
[261] Goodman J W. Statistical optics[M]. New York: Wiley, 1985.
[262] Frery A C, Freitas C C, Sant’Anna S J S. Alternative distributions for the multiplicative model in SAR images[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 169-171.
[263] Frery A C, et al. A model for extremely heterogeneous clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 648-659.
[264] Muller H J. Modeling of extremely heterogeneous radar backscatter[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’97)[C], Singapore, 1997: 1603-1605.
[265] Arsenault H H, April G. Properties of speckle integrated with a finite aperture and logarithmically transformed[J]. Journal of the Optical Society of America, 1976, 66(11): 1160-1163.
[266] Pi Y, Yang X, Liu G. Polarimetric speckle reduction using multi-texture maximum likelihood method[J]. Electronics Letters, 2003, 39(18): 1348-1349.
[267] Novak L M, Burl M C. Optimal speckle reduction in Pol-SAR imagery and itseffect on target detection[A]. In: Proc. SPIE Vol. 1101 Millimeter Wave and Synthetic Aperture Radar[C], Orlando, FL, USA, 1989: 84-115.
[268] Novak L M, et al. Optimal polarimetric processing for enhanced target detection[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’91)[C], Espoo, Finland, 1991: 69-75.
[269]《实用积分表》编委会.实用积分表[M].合肥:中国科学技术大学出版社, 2006.
[270] Salazar II J S. Detection schemes for synthetic aperture radar imagery based on a beta prime statistical model[D]. Albuquerque, New Mexico, USA: The University of New Mexico, 1999.
[271] Jacobo-Berlles J. Nuevas Familias De Distribuciones Polarimetricas Para Imagenes Sar[D]. Buenos Aires, Argentina: Universidad de Buenos Aires, 2005.
[272]孙即祥.现代模式识别[M].长沙:国防科技大学出版社, 2002.
[273]杨春,倪勤.变步长非单调模式搜索法[J].高等学校计算数学学报, 2005, 27(2): 160-168.
[274]蔡均猛等.模式搜索法在生物质热解动力学中的应用[J].动力工程, 2005, 25(5): 737-741.
[275]刘守生.遗传算法与小波神经网络中若干问题的研究[D].南京:南京航空航天大学, 2004.
[276]师学明,王家映.地球物理资料非线性反演方法讲座(三):模拟退火法[J].工程地球物理学报, 2007, 4(3): 165-174.
[277]吴翊,李永乐,胡庆军.应用数理统计[M].长沙:国防科技大学出版社, 2003.
[278]吴昊.高光谱遥感图像数据分类技术研究[D].长沙:国防科技大学, 2004.
[279] (美)Duda R O, Hart P E, Stork D G著,李宏东,姚天翔等译.模式分类[M].北京:机械工业出版社, 2006.
[280]万余庆等.高光谱遥感应用研究[M].北京:科学出版社, 2006.
[281] Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance[J]. International Journal of Remote Sensing, 2007, 28(5): 823-870.
[282] Congalton R G. A review of assessing the accuracy of classification of remotely sensed data[J]. Remote Sensing of Environment, 1991, 3735-46.
[283] Foody G M. Status of land cover classification accuracy assessment[J]. Remote Sensing of Environment, 2002, 80: 185-201.
[284] Shahshahani B M, Landgrebe D A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J]. IEEETransactions on Geoscience and Remote Sensing, 1994, 32(5): 1087-1095.
[285] Jackson Q, Landgrebe D A. Adaptive Bayesian contextual classification based On Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2454-2463.
[286] Derin H, Kelly P A. Discrete-index Markov-type random processes[J]. Proceedings of the IEEE, 1989, 77(10): 1485-1510.
[287] Besag J. On the statistical analysis of dirty pictures[J]. Journal of the Royal Statistical Society, 1986, 48(3): 259-302.
[288] Zebker H A, van Zyl J J, Held D N. Imaging radar polarimetry from wave synthesis[J]. Journal of Geophysical Research, 1987, 92(B1): 683-701.
[289] Durden S L, van Zyl J J, Zebker H A. The unpolarized component in polarimetric radar observations of forested areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(2): 268-271.
[290]孙即祥.数字图象处理[M].石家庄:河北教育出版社, 1993.
[291] Evans D L, et al. Radar polarimetry: analysis tools and applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(6): 774-789.
[292] Cloude S R. Uniqueness of target decomposition theorems in radar polarimetry[R]. Direct and Inverse Methods in Radar Polarimetry, Part 1, NATO-ARW, Boerner W-M et al., Eds. Norwell, MA: Kluwer Academic Publishers, 1992.
[293] Holm W A, Barnes R M. On radar polarization mixed target state decomposition techniques[A]. In: Proc. IEEE National Radar Conference[C], Ann Arbor, MI, USA, 1988: 249-254.
[2]张直中.微波成像技术[M].北京:科学出版社, 1990.
[3]保铮等.雷达成像技术[M].北京:电子工业出版社, 2003.
[4]刘永坦.雷达成像技术[M].哈尔滨:哈尔滨工业大学出版社, 1999.
[5] Young S H H. Gallery of USAF weapons: 2007 USAF almanac[J]. Air Force Magazine, May, 2007: 135-158.
[6] van Zyl J J, Zebker H A, Elachi C. Imaging radar polarization signatures: theory and observation[J]. Radio Science, 1987, 22(4): 529-543.
[7] Zebker H A, van Zyl J J. Imaging radar polarimetry: a review[J]. Proceedings of the IEEE, 1991, 79(11): 1583-1606.
[8] Touzi R, et al. A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction[J]. Canadian Journal of Remote Sensing, 2004, 30(3): 380-407.
[9] Lee J S, et al. A review of polarimetric SAR algorithms and their applications[J]. Taiwan Journal of Photogrammetry and Remote Sensing, 2004, 9(3): 31-80.
[10]庄钊文,肖顺平,王雪松.雷达极化信息处理及其应用[M].北京:国防工业出版社, 1999.
[11] Kennaugh E M. Polarization properties of radar reflections[D]. Columbus, OH, USA: The Ohio State University, 1952.
[12] van Zyl J J. On the importance of polarization in radar scattering problems[D]. Pasadena, CA, USA: California Institute of Technology, 1985.
[13] Agrawal A P. A polarimetric rain backscatter model developed for coherent polarization diversity radar applications[D]. Chicago, IL, USA: University of Illinois, 1986.
[14] Yang J. On theoretical problems in radar polarimetry[D]. Niigata-shi, Japan: Niigata University, 1999.
[15]苏瑞龙.基因演绎法于全偏极合成孔径雷达影像对比强化最优化之研究[D].台湾:国立中央大学, 2003.
[16] Agrawal A P, Boerner W-M. Redevelopment of Kennaugh’s target characteristic polarization state theory using the polarization transformation ration formalism for the coherent case[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 2-14.
[17] Boerner W-M, et al. On the basic principles of radar polarimetry: the targetcharacteristic polarization state theory of Kennaugh, Huynen’s polarization fork concept, and its extension to the partially polarized case[J]. Proceedings of the IEEE, 1991, 79(10): 1538-1550.
[18] Huynen J R. Phenomenological theory of radar targets[D]. Delft, The Netherlands: Technical University of Delft, 1970.
[19] Cloude S R, Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
[20] Cameron W L, Youssef N N, Leung L K. Simulated polarimetric signatures of primitive geometrical shapes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 793-803.
[21] Cloude S R, Pottier E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 549-557.
[22] Freeman A, 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.
[23] Dong Y, Forster B C, Ticehurst C. A new decomposition of radar polarization signatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 933-939.
[24] Touzi R, Charbonneau F. Characterization of target symmetric scattering using polarimetric SARs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2507-2516.
[25] Yamaguchi Y, et al. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699-2005.
[26] Xu F, Jin Y Q. 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.
[27] Cameron W L, Rais H. Conservative polarimetric scatterers and their role in incorrect extensions of the Cameron decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12): 3506-3516.
[28] Yamaguchi Y, Yajima Y, Yamada H. A four-component decomposition of POLSAR images based on the coherency matrix[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(3): 292-296.
[29] Touzi R. Target scattering decomposition in terms of roll-invariant target parameters[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1): 73-84.
[30] Lee J S, et al. Polarization orientation estimation and applications: a review[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 428-430.
[31] Pottier E, et al. Estimation of the terrain surface azimuthal/range slopes using polarimetric decomposition of PolSAR Data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999:2212-2214.
[32] Lee J S, Schuler D L, Ainsworth T L. Polarimetric SAR data compensation for terrain azimuth slope variation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2153-2163.
[33] Lee J S, et al. On the estimation of radar polarization orientation shifts induced by terrain slopes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(1): 30-41.
[34] Schuler D L, Lee J S, De Grandi G. Measurement of topography using polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(5): 1266-1277.
[35] Schuler D L, et al. Terrain topography measurement using multipass polarimetric synthetic aperture radar data[J]. Radio Science, 2000, 35(3): 813-832.
[36] Novak L M, Burl M C. Optimal speckle reduction in polarimetric SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(2): 293-305.
[37] Lee J S, Grunes M R, Mango S A. Speckle reduction in multipolarization, multifrequency SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1991, 29(4): 535-544.
[38] Goze S, Lopes A. A MMSE speckle filter for full resolution SAR polarimetric data[J]. Journal of Electromagnetic Waves and Applications, 1993, 7(5): 717-737.
[39] Touzi R, Lopes A. The principle of speckle filtering in polarimetric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1110-1114.
[40] Fleischman J G, et al. Multichannel whitening of SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(1): 156-166.
[41] Lopes A, Sery F. Optimal speckle reduction for the product model in multilook polarimetric SAR imagery and the Wishart distribution[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 632-647.
[42] Liu G, et al. The multilook polarimetric whitening filter (MPWF) for intensity speckle reduction in polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 1016-1020.
[43] Lee J S, Grunes M R, De Grandi G. Polarimetric SAR speckle filtering and itsimplication for classfication[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2363-2373.
[44] Schou J, Skriver H. Restoration of polarimetric SAR images using simulated annealing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(9): 2005-2016.
[45] Touzi R. A review of speckle filtering in the context of estimation theory[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2392-2404.
[46] Lopez-Martinez C, Fabregas X. Polarimetric SAR speckle noise model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2232-2242.
[47] Gu J, et al. Speckle filtering in polarimetric SAR data based on the subspace decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1635-1641.
[48] Lee J S, et al. Scattering-model-based speckle filtering of polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(1): 176-187.
[49] Lopez-Martinez C. Multidimensional speckle noise, modelling and filtering related to SAR data[D]. Barcelona, Spain: Technical University of Catalonia, 2003.
[50] Novak L M, Burl M C. Studies of target detection algorithms that use polarimetric radar data[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 150-165.
[51] Chaney R D, Burl M C, Novak L M. On the performance of polarimetric target detection algorithms[J]. IEEE Transactions on Aerospace and Electronic Systems Magazine, 1990, 5(11): 10-15.
[52] Novak L M, Burl M C. Optimal polarimetric processing for enhanced target detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 234-244.
[53] Touzi R. On the use of polarimetric SAR data for ship detection[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 812-814.
[54] Touzi R. Calibrated polarimetric SAR data for ship detection[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’00)[C], Honolulu, HI, USA, 2000: 144-146.
[55] Conradsen K, et al. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1): 4-19.
[56] Schou J, et al. CFAR edge detector for polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1): 20-32.
[57] Touzi R, et al. Ship detection and characterization using polarimetric SAR[J]. Canadian Journal of Remote Sensing, 2004, 30(3): 552-559.
[58] Kong J A, et al. Identification of terrain cover using the optimal polarimetric classifier[J]. Journal of Electromagnetic Waves and Applications, 1988, 2(2): 171-194.
[59] Yueh H A, et al. Bayes classification of terrain cover using normalized polarimetric data[J]. Journal of Geophysical Research, 1988, 93(B12): 15261-15267.
[60] Lim H H, et al. Classification of earth terrain using polarimetric synthetic aperture radar images[J]. Journal of Geophysical Research, 1989, 94(B6): 7049-7057.
[61] van Zyl J J, Burnette C F. Bayesian classification of polarimetric SAR images using adaptive a priori probability[J]. International Journal of Remote Sensing, 1992, 13(5): 835-840.
[62] Lee 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.
[63] Chen K S, et al. Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 814-820.
[64] Benz U C. Supervised fuzzy analysis of single- and multichannel SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 1023-1037.
[65] Fukuda S, Hirosawa H. A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2282-2286.
[66] Keshava N, Moura J M F. Matching wavelet packets to Gaussian random processes[J]. IEEE Transactions on Signal Processing, 1999, 47(6): 1604-1614.
[67] Lee J S, Grunes M R, Pottier E. Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2343-2351.
[68] Chen C T, Chen K S, Lee J S. The use of fully polarimetric information for the fuzzy neural classification of SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2089-2100.
[69] Pellizzeri T M, et al. Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2338-2353.
[70] Lombardo P, et al. Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas[J]. IEEE Transactions onGeoscience and Remote Sensing, 2003, 41(9): 1959-1975.
[71] Kouskoulas Y, Ulaby F T, Pierce L E. The Bayesian hierarchical classifier (BHC) and its application to short vegetation using multifrequency polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(2): 469-477.
[72] van Zyl J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36-45.
[73] Rignot E, Chellappa R, Dubois P. Unsupervised segmentation of polarimetric SAR data using the covariance matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 697-705.
[74] Rignot E, Chellappa R. Segmentation of polarimetric synthetic aperture radar data[J]. IEEE Transactions on Image Processing, 1992, 1(3): 281-300.
[75] Wong Y F, Posner E C. A new clustering algorithm applicable to multispectral and polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(3): 634-644.
[76] Pierce L E., et al. Knowledge-based classification of polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1081-1086.
[77] Hara Y, et al. Application of neural networks to radar image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(1): 100-109.
[78] Lee J S, 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.
[79] Ferro-Famil L, Pottier E, Lee J S. Unsupervised classfication of multifrequency and fully polarimetric SAR images based on the H/A/Alpha - Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2332-2342.
[80] Dong Y, Milne A K, Forster B C. Segmentation and classification of vegetated areas using polarimetric SAR image data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(2): 321-329.
[81] Lee J S, et al. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722-731.
[82] Kersten P R, Lee J S, Ainsworth T L. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 519-527.
[83] Hoekman D H, Quinones M J. Land cover type and biomass classification using AirSAR data for evaluation of monitoring scenarios in the Colombian Amazon[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(2): 685-696.
[84] Trizna D B, et al. Projection pursuit classification of multiband polarimetric SARland images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2380-2386.
[85] Hoekman D H, Quinones M J. Biophysical forest type characterization in the Colombian Amazon by airborne polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(6): 1288-1300.
[86] De Grandi G, et al. Texture and speckle statistics in polarimetric SAR synthesized images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2070-2088.
[87] Beaulieu J-M, Touzi R. Segmentation of textured polarimetric SAR scenes by likelihood approximation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2063-2072.
[88] Barnes C F, Burki J. Late-season rural land-cover estimation with polarimetric-SAR intensity pixel blocks andσ-tree-structured near-neighbor classifiers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9): 2384-2392.
[89] Alberga V. Comparison of polarimetric methods in image classification and SAR interferometry applications[D]. Chemnitz, Saxony, Germany: Technical University of Chemnitz, 2004.
[90] Kimura K. A study on target classification/detection in polarimetric SAR image data[D]. Niigata-shi, Japan: Niigata University, 2005.
[91] Dubois P C, van Zyl J J, Engman T. Measuring soil moisture with imaging radars[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4): 915-926.
[92] Mattia F, et al. The effect of surface roughness on multifrequency polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 954-966.
[93] Hajnsek I, Pottier E, Cloude S R. Inversion of surface parameters from polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 727-744.
[94] Hajnsek I. Inversion of surface parameters using polarimetric SAR[D]. Jena, Germany: Friedrich-Schiller University Jena, 2001.
[95] Lopez-Sanchez J M. Analysis and estimation of biophysical parameters of vegetation by radar polarimetry[D]. Valencia, Spain: Universidad Politecnica de Valencia, 1999.
[96] Cloude S R, Papathanassiou K P. Polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1551-1565.
[97] Papathanassiou K P, Cloude S R. Single-baseline polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(11): 2352-2363.
[98]郭华东等.极化干涉雷达遥感机制及应用[J].遥感学报, 2002, 6(6): 401-405.
[99]李新武.极化干涉SAR信息提取方法及其应用研究[D].北京:中国科学院, 2002.
[100] Reigber A, Moreira A. First demonstration of Airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2142-2152.
[101] Reigber A. Airborne polarimetric SAR tomography[D]. Stuttgart, Germany: University of Stuttgart, 2001.
[102] Lombardini F, Reigber A. Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 2014-2016.
[103]郭华东.雷达对地观测理论与应用[M].北京:科学出版社, 2000.
[104]谭衢霖,胡吉平.成像雷达遥感的生态学应用[J].遥感技术与应用, 2005, 20(3): 375-380.
[105]谭衢霖,邵芸.成像雷达(SAR)遥感地质应用综述[J].地质找矿论丛, 2003, 18(1): 59-65.
[106] ESA. Input data sources: airborne missions[EB/OL]. http://earth.esa.int/polsarpro/input.html, 2006-12-20.
[107] van Zyl J, et al. The NASA/JPL three-frequency polarimetric AirSAR system[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’92)[C], Houston, TX, USA, 1992: 649-651.
[108] Chu A, et al. The NASA/JPL AirSAR integrated processor[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’98)[C], Washington, USA, 1998: 1908-1910.
[109] Livingstone C E, et al. CCRS/DREO synthetic aperture radar polarimetry - status report[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’90)[C], Maryland, USA, 1990: 1671-1674.
[110] Livingstone C E, et al. The Canadian airborne R&D SAR facility: the CCRS C/X SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’96)[C], Nebraska, USA, 1996: 1621-1623.
[111] Horn R, Werner M, Mayr B. Extension of the DLR airborne synthetic aperture radar, E-SAR, to X-band[a]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’90)[C], Maryland, USA, 1990: 2047-2049.
[112] Horn R. The DLR airborne SAR project E-SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’96)[C], Nebraska, USA, 1996: 1624-1628.
[113] Scheiber R, et al. Overview of interferometric data acquisition and processing modes of the experimental airborne SAR system of DLR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 35-37.
[114] Skou N, et al. A high resolution polarimetric L-band SAR - design and first results[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 1779-1782.
[115] Christensen E L, et al. EMISAR: an absolutely calibrated polarimetric L- and C-band SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(6): 1852-1865.
[116] Christensen E L, Dall J. EMISAR: a dual-frequency, polarimetric airborne SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 1711-1713.
[117] Uratsuka S, et al. High-resolution dual-bands interferometric and polarimetric airborne SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002:1720-1722.
[118] Uratsuka S, et al. Disastrous environment after earthquake observed by airborne SAR (Pi-SAR)[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 4081-4083.
[119] ESA. Input data sources: spacaeborne missions[EB/OL]. http://earth.esa.int/polsarpro/input_space.html, 2006-12-20.
[120] Jordan R L, Huneycutt R L, Werner M. The SIR-C/X-SAR synthetic aperture radar system[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4): 829-839.
[121] Vant M, Livingstone C, Rey M. Canadian experience on Radarsat-1 and Radarsat-2 GMTI for surveiliance[A]. In: Proc. AIAA/ICAS International Air and Space Symposium and Exposition: The Next 100 Year[C], Dayton, OH, USA , 2003:1-10.
[122] van der Sanden J J, Thomas S J. Applications potential of Radarsat-2-supplement one[R]. Ottawa, Canada: Natural Resources Canada, Canada Centre for Remote Sensing, 2004.
[123] Lee J S, et al. Statistical analysis and segmentation of multi-look SAR imagery using partial polarimetric data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95), Chengdu, China, 1995:1422-1424.
[124] Lee J S, et al. Quantitative comparison of classification capability: fully-polarimetric versus partially polarimetric SAR[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’00)[C], Honolulu, HI, USA, 2000:1101-1103.
[125] Ainsworth T L, Lee J S. Optimal image classification employing“optimal”polarimetric variables[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003:696-698.
[126] Ainsworth T. L, Lee J S. Polarimetric SAR image classification employing subaperture polarimetric analysis[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005:48-50.
[127] van Zyl J J. Application of Cloude’s target decomposition theorem to polarimetric imaging radar data[A]. In: Proc. SPIE Vol. 1748 Radar Polarimetry[C], San Diego, CA, USA, 1992: 184-212.
[128] Burl M C, Novak L M. Polarimetric segmentation of SAR imagery[A]. In: Proc. SPIE Vol. 1471 Automatic Object Recognition[C], Orlando, FL, USA, 1991: 92-115.
[129] Pottier E, Saillard J. On radar polarization target decomposition theorems with application to target classification by Using Network Method[A]. In: Proc. ICAP’91[C], York, England, 1991: 265-268.
[130] Pottier E. Classification of earth terrain in polarimetric SAR images using neural nets modelization[A]. In: Proc. SPIE Vol. 1748 Radar Polarimetry[C], San Diego, CA, USA, 1992: 321-332.
[131] Pottier E. Radar target decomposition theorems and unsupervised classification of full polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994: 1139-1141.
[132] Pottier E, Cloude S R. Unsupervised classification of full polarimetric SAR data and feature vectors identification using radar target decomposition theorems and entropy analysis[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 2247-2249.
[133] Pottier E, Cloude S R. Application of the H/A/αpolarimetric decomposition theorems for land classification[A]. In: Proc. SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry[C], San Diego, CA, USA, 1997: 132-143.
[134] Hellmann M, Jager G, Pottier E. Fuzzy clustering and interpretation of fully polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’01), Sydney, Australia, 2001: 2790-2792.
[135] Cloude S R. An entropy based classification scheme for polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995:2000-2002.
[136] Lombardo P. Optimal classification of polarimetric SAR images using segmentation[A]. In: Proc. IEEE on Radar Conference[C], Long Beach, CA, USA, 2002: 8-13.
[137] Pellizzeri T M, Lombardo P, Ferriero P. Polarimetric SAR image processing: Wishart vs“H/A/alpha”segmentation and classification schemes[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 3976-3978.
[138] Hellmann M, et al. Classification of full polarimetric SAR-data using artificial neural networks and fuzzy Algorithms[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 1995-1997.
[139] Alberga V, Satalino G, Staykova D K. Polarimetric SAR observables for land cover classification: analyses and comparisons[A]. In: Proc. SPIE Vol. 6363 SAR Image Analysis, Modeling, and Techniques VIII[C], Stockholm, Sweden, 2006: 636305.
[140] Fukuda S, Suwa K, Hirosawa H. Texture and statistical distribution in high resolution polarimetric SAR images[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 1268-1270.
[141] Fukuda S, Hirosawa H. Support vector machine classification of land cover: application to polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’01)[C], Sydney, Australia, 2001: 187-189.
[142] Fukuda S, Katagiri R, Hirosawa H. Unsupervised approach for polarimetric SAR image classification using support vector machines[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 2599-2601.
[143] Xu J Y, Yang J, Peng Y N. New method of feature extraction in polarimetric SAR image classification[A]. In: Proc. SPIE Vol. 4741 Battlespace Digitization and Network-Centric Warfare II[C], Orlando, FL, USA, 2002: 337-344.
[144] Xu J Y, et al. Using cross-entropy for polarimetric SAR image classification[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 1917-1919.
[145]徐俊毅,杨健,彭应宁.双波段极化雷达遥感图像分类的新方法[J].中国科学(E辑), 2005, 35(10): 1083-1095.
[146]徐丰,金亚秋.目标散射的去取向理论和应用(一)去取向分析[J].电波科学学报, 2006, 21(1): 6-15.
[147]徐丰,金亚秋.目标散射的去取向理论和应用(二)地表分类应用[J].电波科学学报, 2006, 21(2): 153-160.
[148] Jin Y Q, Chen F. Polarimetric scattering indexes and information entropy of theSAR imagery for surface monitoring[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2502-2506.
[149] Jn Y Q, Xu F. A new set of the parameters for the terrain surface classification in polarimetric SAR image based on deorientation of polarimetric scattering vector[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’06), Denver, CO, USA, 2006: 1403-1406.
[150]金亚秋,陈扉. SAR图像中极化散射指数和信息熵及其地表识别应用[J].自然科学进展, 2003, 13(2): 174-178.
[151]王之禹,朱敏慧,白有天.基于Mueller矩阵分解的非监督聚类算法[J].电子与信息学报, 2001, 23(5): 454-459.
[152]刘秀清,杨汝良.基于全极化SAR非监督分类的迭代分类方法[J].电子学报, 2004, 32(12): 1982-1986.
[153]刘秀清,杨汝良,杨震.双波段全极化SAR图像非监督分类方法及实验研究[J].电子与信息学报, 2004, 26(11): 1738-1745.
[154]王之禹,朱敏慧,白有天.基于最优状态的多波段全极化SAR数据ML分类方法[J].电子与信息学报, 2001, 23(5): 507-511.
[155]李晓玮,种劲松.基于目标相干散射特性的极化SAR图像分解分类方法[J].遥感技术与应用, 2007, 22(3): 443-448.
[156]戴博伟.多极化合成孔径雷达系统与极化信息处理研究[D].北京:中国科学院, 2000.
[157]刘秀清.全极化合成孔径雷达极化信息处理技术研究[D].北京:中国科学院, 2004.
[158]王之禹.全极化合成孔径雷达在地物分类领域的应用研究[D].北京:中国科学院, 2001.
[159]王翠珍.极化SAR数据分析与目标信息提取[D].北京:中国科学院, 1999.
[160] Tzeng Y C, et al. A dynamic learning neural network for remote sensing applications[J]. IEEE Transactions On Geoscience And Remote Sensing, 1994, 32(5): 1096-1102.
[161]陈家堂.全偏极合成孔径雷达于目标分类之研究[D].台湾:国立中央大学, 2002.
[162]林佳驹.以特征为基础的模糊演算算法进行多波段-多极化NASA/JPL POLSAR影像分类之研究[D].台湾:国立成功大学, 2003.
[163]李一麟.空载全偏极POLSAR目标分类之DLBP演算法[D].台湾:国立成功大学, 2004.
[164] Liu G, et al. Bayesian classification of multi-look polarimetric SAR images witha generalized multiplicative speckle model[A]. In: Proc. SPIE Vol. 3070 algorithms for synthetic aperture radar imagery IV[C], Orlando, FL, USA, 1997: 398-405.
[165]刘国庆等.多视极化合成孔径雷达图象的分类和极化通道优化[J].电子科学学刊, 1998, 20(1): 56-61.
[166]刘国庆,熊红,黄顺吉.基于小波变换和马尔可夫随机场的极化SAR图像自动分类[J].电子科学学刊, 2000, 22(3): 359-365.
[167]谢艳.极化合成孔径雷达分类[D].成都:电子科技大学, 2006.
[168]吴永辉,计科峰,郁文贤.基于支持向量机的极化SAR图像分类[J].现代雷达, 2007, 29(6): 57-73.
[169]吴永辉,计科峰,郁文贤.基于H-α和改进C-均值的全极化SAR图像非监督分类[J].电子与信息学报, 2007, 29(1): 30-34.
[170]吴永辉.极化SAR图像分类技术研究[D].长沙:国防科技大学, 2007.
[171]代大海. POLSAR图像模拟及目标检测与分类方法研究[D].长沙:国防科技大学, 2003.
[172] Andreadis A, et al. Adaptive segmentation of multiband polarimetric SAR images for the identification of archaeological resources[A]. In: Proc. SPIE Vol. 2960 Remote Sensing for Geography, Geology, Land Planning, and Cultural Heritage [C], Taormina, Italy, 1996:148-152.
[173] Qong M, et al. Preliminary results from the model-based classification of airborne polarimetric SAR images[A]. In: Proc. SPIE Vol. 4152 Microwave Remote Sensing of the Atmosphere and Environment II[C], Sendai, Japan, 2000: 327-338.
[174] Kersten P R, Lee R R-Y. Segmentation polarimetric SAR images using robust competitive clustering[R]. Patuxent River, MD, USA: Naval Air Warfare Center Aircraft Division, 2001.
[175] Beaulieu J-M, Touzi R. Hierarchical segmentation of polarimetric SAR images[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02), Toronto, Canada, 2002: 2590-2592.
[176] Skriver H, et al. Polarimetric segmentation using Wishart test statistic. In: Proc. Internatonal Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 1011-1013.
[177] El Assad S, Saad A, Barba D. Unsupervised optimal fuzzy clustering and Markov segmentation of polarimetric radar imaging[A]. In: Proc. SPIE Vol. 2958 Microwave Sensing and Synthetic Aperture Radar[C], Taormina, Italy, 1996: 186-197.
[178] Borghys D, et al. Supervised feature-based classification of multi-channel SAR images using logistic regression[J]. 2000.
[179] Chen C H, Du Y. A multiresolution wavelet analysis for SAR image segmentation using statistical separability measures[A]. In: Proc. Part of the EUROPTO Conference on Image and Signal Processing for Remote Sensing[C], Barcelona, Spain, 1998: 104-110.
[180] Khan K U, Yang J. Novel features for polarimetric SAR images classification by neural network[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 165-170.
[181] Huynen J R. The Stokes matrix parameters and their interpretation in terms of physical target properties[A]. In: Proc. SPIE Vol. 1317 Polarimetry: Radar, Infrared, Visible, Ultraviolet, and X-Ray[C], Huntsville, AL, USA, 1990: 195-207.
[182] Pottier E. On Dr. J. R. Huynen’s main contributions in the development of the polarimetric radar techniques, and how the‘radar targets phenomenological concept’becomes a theory[A]. In: Proc. SPIE Radar Polarimetry[C], San Diego, CA, USA, 1992: 72-85.
[183] Huynen J R. Physical reality of radar target[A]. In: Proc. SPIE Vol. 1748 Radar Polarimetry[C], San Diego, CA, USA, 1992: 86-96.
[184] Huynen J R. A new extended target decomposition scheme[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994: 1124-1125.
[185] Yang J, et al. On Huynen's decomposition of a Kennaugh matrix[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(3): 369-372.
[186] Krogager E. New decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26: 1525-1527.
[187] Krogager E. Decomposition of the radar target scattering matrix with application to high resolution target imaging[A]. In: Proc. IEEE National Telesystems Conference (NTC’91)[C], Atlanta, GA, USA, 1991: 0077-0082.
[188] Krogager E. Wideband Pol-SAR/ISAR signal and image processing for target identification and discrimination[A]. In: Proc. IEEE Asia-Pacific Microwave Conference[C], Adelaide, Melbourne, Australia, 1992: 947-950.
[189] Krogager E. Aspects of polarimetric radar target imaging[D]. Lyngby, Denmark: Technical University of Denmark, 1993.
[190] Krogager E, Czyz Z H. Properties of the sphere, diplane, and helix decomposition[A]. In: Proc. Third International Workshop on Radar Polarimetry[C], IRESTE, University of Nantes, France, 1995: 106-114.
[191] Krogager E, Boerner W-M, Madsen S N. Feature-motivated Sinclair matrix (sphere/diplane/helix) decomposition and its application to target sorting for land feature classification[A]. In: Proc. SPIE Conference on Wideband InterferometricSensing and Imaging Polarimetry[C], San Diego, CA, USA, 1997: 144-154.
[192] Cameron W L, Leung L K. Feature motivated polarization scattering matrix decomposition[A]. In: Record of the IEEE 1990 International Radar Conference[C], Arlington, VA, USA, 1990: 549-557.
[193] Cloude S R. Radar target decomposition theorems[J]. Electronics Letters, 1985, 21(1): 22-24.
[194] Cloude S R. Target decomposition theorems applied to multiple scattering of polarised waves[A]. In: Proc. Interational Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994: 1126-1128.
[195] Praks J, et al. SAR target decomposition for stochastic natural targets for L- and C-band[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99)[C], Hamburg, Germany, 1999: 1558-1560.
[196] Praks J, Hallikainen M. A novel approach in polarimetric cvariance matrix eigendecomposition[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’00)[C], Honolulu, HI, USA, 2000: 1119-1121.
[197] Ainsworth T L, Cloude S R, Lee J S. Eigenvector analysis of polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02)[C], Toronto, Canada, 2002: 626-628.
[198] Yang J, et al. New formula of the polarization entropy[J]. IEICE Transactions on Communication, 2006, E89-B(3): 1033-1035.
[199] Freeman A, Durden S. A three-component scattering model to describe polarimetric SAR data[A]. In: Proc. SPIE Conference on Radar Polarimetry[C], San Diego, CA, USA, 1992: 213-224.
[200] Moghaddam M, Freeman A. Modification to the three-component lassification algorithm for SAR data[A]. In: Proc. Progress in Electromagnetics Research Symposium[C], Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, 1993: 164.
[201] Freeman A. Fitting a two-component scattering model to polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’99), Hamburg, Germany, 1999: 2649-2651.
[202] Qong M. Scattering mechanism identification based on the rotation and eccentric angles of polarimetric SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’04)[C], Anchorage, AK, USA, 2004: 3054-3057.
[203] Aiazzi B, et al. Land cover classfication of built-up areas through enhanced fuzzy nearest-mean reclustering of textural features from X- and C-band polarimetric SAR data[A]. In: Proc. SPIE Vol. 5236, SAR Image Analysis, Modeling, and Techniques VI[C], Bellingham, WA, USA, 2004: 105-115.
[204] Lee J S, et al. K-distribution for multi-look processed polarimetric SAR imagery[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94)[C], Pasadena, CA, USA, 1994:2179-2181.
[205] Beaulieu J-M, Touzi R. Segmentation of textured scenes using polarimetric SARs[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03)[C], Toulouse, France, 2003: 446-448.
[206] Freitas C C, Frery A C, Correia A H. The polarimetric G distribution for SAR data analysis[J]. Environmetrics, 2005, 16(1): 13-31.
[207] Frery A, Correia A, Freitas C. Multifrequency full polarimetric SAR classification with multiple sources of statistical evidence[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’06)[C], Denver, CO, USA, 2006: 4195-4197.
[208] Park S-E, Moon W M. Classification of the polarimetric SAR using fuzzy boundaries in entropy and alpha plane[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 5517-5519.
[209] Putignano E, et al. Unsupervised classification of a central Italy landscape by polarimetric L-band SAR data[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 1291-1294.
[210] Pottier E, Lee J S, Ferro-Famil L. Advanced concepts in polarimetry-part 2 (Polarimetric Target Classification)[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
[211] VEXCEL. Earthview Matrix. http://www.vexcel.com, 2005.
[212] PCI Geomatics. Pcigeomatics ? SAR polarimetry workstation. http://www.ocigeomatics.com, 2008.
[213] Pottier E, et al. PolSARpro v2.0: the polarimetric SAR data processing and educational toolbox[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’05)[C], Seoul, Korea, 2005: 3173-3176.
[214] Lee J S, et al. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1017-1028.
[215] Boerner W-M. Basics of SAR polarimetry I[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
[216] Boerner W-M. Basics of SAR polarimetry II[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
[217] Boerner W-M. Recent advances in extra-wide-band polarimetry, interferometry and polarimetric interferometry in synthetic aperture remote sensing, and its applications[J]. IEE Proceedings-Radar Sonar Navigation, Special Issue of the EUSAR-02, 2003, 150(3): 113-125.
[218] Boerner W-M. Recent advances in radar polarimetry and polarimetric SAR interferometry[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2004.
[219] Sinclair G. The transmission and reception of elliptically polarized waves[J]. Proceedings of the IRE, 1950, 38(2): 148-151.
[220] Deschamps G A. Geometrical representation of the polarization of a plane electromagnetic wave[J]. Proceedings of the IRE, 1951, 39(5): 540-544.
[221] Gent H. Elliptically polarized waves and their reflections from radar targets: a theoretical analysis[R]. Chelenham, England, UK: Telecommunications Research Establishment, 1954.
[222] Copeland J R. Radar target classification by polarization properties[J]. Proceedings of the IRE, 1960, 48(7): 1290-1296.
[223] Bickel S H. Some invariant properties of the polarization scattering matrix[J]. Proceedings of the IEEE, 1965, 53: 1970-1072.
[224] Boerner W-M, et al. Polarization dependence in electromagnetic inverse problems[J]. IEEE Transactions on Antennas and Propagation, 1981, AP-29(2): 262-271.
[225]王雪松.宽带极化信息处理的研究[D].长沙:国防科技大学, 1999.
[226]刘涛.瞬态极化统计理论及应用研究[D].长沙:国防科技大学, 2007.
[227]施龙飞.雷达极化抗干扰技术研究[D].长沙:国防科技大学, 2007.
[228]代大海.极化雷达成像及目标特征提取研究[D].长沙:国防科技大学, 2008.
[229] Guissard A. Mueller and Kennaugh matrices in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3): 590-597.
[230] Kostinski A B, Boerner W-M. On foundations of radar polarimetry[J]. IEEE Transactions on Antennas Propagation, 1986, AP-34(12): 1395-1404.
[231] Mieras H, Kostinski A B, Boerner W-M. Comments, with reply, on“on foundations of radar polarimetry”[J]. IEEE Transactions on Antennas Propagation, 1986, 34(12): 1470-1473.
[232] Hubbert J C. A comparison of radar, optic, and specular null polarization theories[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3): 658-671.
[233] Hubbert J C, Bringi V N. Specular null polarization theory: applications to radar meteorology[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 859-873.
[234] Luneburg E. Comments on“the specular null polarization theory”[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 1070-1071.
[235] Hubbert J C. Reply to“comments on‘the specular null polarization theory’”[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 1071-1072.
[236] Graves C S. Radar polarization power scattering matrix[J]. Proceedings of the IRE, 1956, 44(2): 248-252.
[237] Luneburg E, Boerner W-M. Kennaugh and the dual space approach to radar polarimetry[A]. In: Proc. IEEE Antennas and Propagation Society International Symposium[C], Columbus, OH, USA, 2003: 190-193.
[238] Luneburg E. Principles of radar polarimetry[J]. IEICE Transactions on Electronics (Special Issue on Electromagnetic Theory), 1995, E78-C(10): 1339-1345.
[239] Luneburg E. Directional Jones and Stokes vectors[R]. George Schmid Weg 4, Wessling, Germany: EML Consultants, 2005.
[240] Luneburg E, Cloude S R. Radar versus optical polarimetry[A]. In: Proc. SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry[C], San Diego, CA, USA, 1997: 361-372.
[241] Wigner E P. Normal form of antiunitary operators[J]. Journal of Mathematical Physics, 1960, 1(5): 409-413.
[242]陈军.光学位相共轭及其应用[M].北京:科学出版社, 1999.
[243] Luneburg E. Aspects of Radar Polarimetry[J]. Elektrik-Turkish Journal of Electrical Engineering & Computer Sciences (Special Issue on Electromagnetic Problems and Numerical Simulation Techniques Current Status - Future Trends), 2002, 10(2): 219-243.
[244] Keaton P W. Time reversal in polarization phenomena of nuclear interactions[R]. Los Alamos, NM, USA: Los Alamos Scientific Laboratory of the University of California, 1970.
[245] Meyer C D. Matrix analysis and applied linear algebra[M]. Philadelphia, PA, USA: Society for Industrial & Applied Mathematics (SIAM), 2004.
[246] Born M, Wolf E. Principles of optics: electromagnetic theory of propagation, interference and diffraction of light (7th Ed.)[M]. Cambridge, UK: Cambridge University Press, 1999.
[247] Gambini J, et al. Polarimetric SAR region boundary detection using B-spline deformable countours under the GH Model. In: Proc. XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI’05), Natal, RN, Brazil, 2005: 197-204.
[248] Gambini M J. Modelos De Segmentacion Basados En Regiones Y Contornos Activos Aplicados a Imagenes De Radar De Apertura Sintetica[D]. Buenos Aires, Argentina: Universidad de Buenos Aires, 2006.
[249] (英)丹蒂(Dainty J C)编,黄乐天等译.激光散斑及有关现象[M].北京:科学出版社, 1981.
[250] Goodman J W. Speckle phenomena in optics: theory and applications[M]. Englewood, CO, USA: Roberts & Company Publishers, 2006.
[251]刘培森.散斑统计光学基础[M].北京:科学出版社, 1986.
[252] (美)顾德门(Goodman J W)著,秦克诚等译.统计光学[M].北京:科学出版社, 1992.
[253] Frost V S, et al. A model for radar images and its application to adaptive digital filtering of multiplicative noise[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, PAMI-4(2): 157-165.
[254] Kuan D T, et al. Adaptive noise smoothing filter for imags with signal-dependent noise[J]. IEEE Transactions on Pattern Analysis and Machine Inteligence, 1985, PAMI-7(2): 165-177.
[255] Yoon S-H, Kim Y-S. Classified pixel-based windowing algorithm for polarimetric SAR speckle filtering[J]. Electronics Letters, 2003, 39(1): 115-116.
[256] OliverC, Quegan S. Understanding synthetic aperture radar imags[M]. Boston, MA: Artech House, 1998.
[257]贾承丽,匡纲要. SAR图像去斑方法[J].中国图象图形学报, 2005, 10(2): 135-141.
[258]王程. SAR图像相干抑制和光学图像序列超分辨技术研究[D].长沙:国防科技大学, 2002.
[259]付琨.高分辨率单视单极化SAR图像地物分类方法研究[D].长沙:国防科技大学, 2002
[260]高贵. SAR图像目标ROI自动获取技术研究[D].长沙:国防科技大学, 2007.
[261] Goodman J W. Statistical optics[M]. New York: Wiley, 1985.
[262] Frery A C, Freitas C C, Sant’Anna S J S. Alternative distributions for the multiplicative model in SAR images[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 169-171.
[263] Frery A C, et al. A model for extremely heterogeneous clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 648-659.
[264] Muller H J. Modeling of extremely heterogeneous radar backscatter[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’97)[C], Singapore, 1997: 1603-1605.
[265] Arsenault H H, April G. Properties of speckle integrated with a finite aperture and logarithmically transformed[J]. Journal of the Optical Society of America, 1976, 66(11): 1160-1163.
[266] Pi Y, Yang X, Liu G. Polarimetric speckle reduction using multi-texture maximum likelihood method[J]. Electronics Letters, 2003, 39(18): 1348-1349.
[267] Novak L M, Burl M C. Optimal speckle reduction in Pol-SAR imagery and itseffect on target detection[A]. In: Proc. SPIE Vol. 1101 Millimeter Wave and Synthetic Aperture Radar[C], Orlando, FL, USA, 1989: 84-115.
[268] Novak L M, et al. Optimal polarimetric processing for enhanced target detection[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’91)[C], Espoo, Finland, 1991: 69-75.
[269]《实用积分表》编委会.实用积分表[M].合肥:中国科学技术大学出版社, 2006.
[270] Salazar II J S. Detection schemes for synthetic aperture radar imagery based on a beta prime statistical model[D]. Albuquerque, New Mexico, USA: The University of New Mexico, 1999.
[271] Jacobo-Berlles J. Nuevas Familias De Distribuciones Polarimetricas Para Imagenes Sar[D]. Buenos Aires, Argentina: Universidad de Buenos Aires, 2005.
[272]孙即祥.现代模式识别[M].长沙:国防科技大学出版社, 2002.
[273]杨春,倪勤.变步长非单调模式搜索法[J].高等学校计算数学学报, 2005, 27(2): 160-168.
[274]蔡均猛等.模式搜索法在生物质热解动力学中的应用[J].动力工程, 2005, 25(5): 737-741.
[275]刘守生.遗传算法与小波神经网络中若干问题的研究[D].南京:南京航空航天大学, 2004.
[276]师学明,王家映.地球物理资料非线性反演方法讲座(三):模拟退火法[J].工程地球物理学报, 2007, 4(3): 165-174.
[277]吴翊,李永乐,胡庆军.应用数理统计[M].长沙:国防科技大学出版社, 2003.
[278]吴昊.高光谱遥感图像数据分类技术研究[D].长沙:国防科技大学, 2004.
[279] (美)Duda R O, Hart P E, Stork D G著,李宏东,姚天翔等译.模式分类[M].北京:机械工业出版社, 2006.
[280]万余庆等.高光谱遥感应用研究[M].北京:科学出版社, 2006.
[281] Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance[J]. International Journal of Remote Sensing, 2007, 28(5): 823-870.
[282] Congalton R G. A review of assessing the accuracy of classification of remotely sensed data[J]. Remote Sensing of Environment, 1991, 3735-46.
[283] Foody G M. Status of land cover classification accuracy assessment[J]. Remote Sensing of Environment, 2002, 80: 185-201.
[284] Shahshahani B M, Landgrebe D A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J]. IEEETransactions on Geoscience and Remote Sensing, 1994, 32(5): 1087-1095.
[285] Jackson Q, Landgrebe D A. Adaptive Bayesian contextual classification based On Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2454-2463.
[286] Derin H, Kelly P A. Discrete-index Markov-type random processes[J]. Proceedings of the IEEE, 1989, 77(10): 1485-1510.
[287] Besag J. On the statistical analysis of dirty pictures[J]. Journal of the Royal Statistical Society, 1986, 48(3): 259-302.
[288] Zebker H A, van Zyl J J, Held D N. Imaging radar polarimetry from wave synthesis[J]. Journal of Geophysical Research, 1987, 92(B1): 683-701.
[289] Durden S L, van Zyl J J, Zebker H A. The unpolarized component in polarimetric radar observations of forested areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(2): 268-271.
[290]孙即祥.数字图象处理[M].石家庄:河北教育出版社, 1993.
[291] Evans D L, et al. Radar polarimetry: analysis tools and applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(6): 774-789.
[292] Cloude S R. Uniqueness of target decomposition theorems in radar polarimetry[R]. Direct and Inverse Methods in Radar Polarimetry, Part 1, NATO-ARW, Boerner W-M et al., Eds. Norwell, MA: Kluwer Academic Publishers, 1992.
[293] Holm W A, Barnes R M. On radar polarization mixed target state decomposition techniques[A]. In: Proc. IEEE National Radar Conference[C], Ann Arbor, MI, USA, 1988: 249-254.
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