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雷达极化中若干理论问题研究
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摘要
作为电磁波四个基本特征之一,极化信息的开发利用对于提高雷达探测性能和完善雷达功能均具有重要作用。近年来,随着雷达极化测量技术和SAR成像技术的完善,各种星载和机载POLSAR系统不断涌现,极化信息的应用领域也不断拓展,但现有雷达极化理论尚不成熟、完善,已成为制约极化信息进一步开发利用的瓶颈。为此,本文以完善雷达极化理论和推进实用化为目的,对雷达极化中的目标特征极化、散射建模等若干理论问题进行了深入、系统地研究,并在大量高分辨POLSAR实测数据的支持下,采用理论分析和实验验证的研究方法,讨论了这些理论在POLSAR图像地物分类中的联合应用。
     论文工作的开展基本遵循一致的步骤,即在充分了解前人研究工作、现关成果及存在问题的基础上,找准论文的切入点展开深入的研究。归纳起来,论文主要开展的工作有:
     1)发展和完善了现有相干情形目标特征极化理论。推导了天线接收功率全局最大值及其极值条件,针对现有研究未涉及目标参数变化对特征极化的影响,首次结合功率密度图研究了这一问题,并得出了一些重要的结论,为简化目标特征极化求解、预判目标特征极化位置提供了理论支撑。该方面的研究成果已被《中国科学E辑:技术科学》中英文刊用;
     2)以实用化为目的,对现有单变量Lagrange乘因子求解算法进行了优化,提高了非相干、同极化通道特征极化求解效率。单变量Lagrange乘因子求解实质归结为一个以Lagrange乘因子为自变量的一元六次方程求解,其求解面临两个问题:a)天线接收功率极值与该方程根之间对应关系未知,使得求解过程搜索不必要的根,增加了算法运算量;b)方程根隔根区间未知,造成需人为确定每个根的隔根区间,不利于算法工程实现。为此,文中从两方面入手开展研究:其一,理论分析天线接收功率极值与该方程根之间对应关系,并得出了“天线接收功率最大、最小值分别对应该方程最大、最小根”的结论;其二,理论推导方程最大、最小根的隔根区间。以此为基础,最后给出了优化后的单变量Lagrange乘因子求解具体步骤。该方面的研究成果已连续在《信号处理》、《IEEE Transactions on Geoscience and Remote Sensing Letter》等刊物上发表;
     3)在非相干、任意通道约束特征极化方面,统一了任意通道约束天线接收功率数学模型,从而将不同通道约束特征极化求解归结为同极化通道天线接收功率形式统一求解。利用变极化基技术对该模型进行了简化处理,以此为基础,提出了一种基于区间定位的目标特征极化求解新算法。该算法在理论确定天线接收功率极值在功率密度图上的单调区间基础上采用区间二分法迭代搜索极值位置。实验结果和理论分析表明,该算法较优化单变量Lagrange乘因子求解更具有运算优势。该部分研究成果已在《测绘学报》上发表;
     4)以实用化为目的,提出了一种基于“三步”解耦思想的特征极化求解新方法,解决了非相干、无约束特征极化求解困难的问题。将“三步”解耦思想应用于分布式目标面临两个新问题:a)分布式目标的散射回波始终是部分极化的,而部分极化波又只能采用Stokes矢量表征;b)部分极化波的天线失匹配接收不能简单的将天线极化与入射波正交或相同,因为天线极化为完全极化的。文中首先分析了部分极化波的最优接收问题,得到了任意部分极化波天线接收功率上下限和失匹配接收条件,并指出天线接收功率上下限实质是发射天线极化的函数。以此为依据,给出了非相干、无约束特征极化求解步骤:a)通过天线接收功率上下限函数优化得到发射天线最优极化,即对应天线接收功率下限最小,或上限最大的发射天线极化状态;b)利用散射方程计算最优散射回波;c)利用部分极化波失匹配接收条件得到接收天线最优极化;该部分研究成果已在《信号处理》上刊用;
     5)在详细分析现有目标散射建模研究成果的基础上,得出了“基于H/Alpha的散射描述是现有目标散射描述中最合理的一种”这一结论。但通过对极化散射熵的理论分析发现,H/Alpha散射描述并不总是合理,因为它假设目标总存在占绝对优势的平均散射机制并不总是成立。合理的描述应是对于不同散射随机性的目标,采用不同方式表征其散射:对于低散射随机性目标,可采用主散射机制表征;对于中散射随机性目标,应采用主散射机制和次要散射机制联合表征;对于高散射随机性目标,其散射已无法采用某种散射机制表征。这一研究为后续的散射特征提取和散射分类提供了理论依据。该方面研究成果已被国际权威杂志《IEEE Transaction on Geoscience and Remote Sensing》建议小修后刊用,获得了审稿专家很好的评价;
     6)发展和完善了散射相似性理论。针对现有散射相似性无法应用于分布式目标的局限,定义了一种度量目标散射相似性的新参数。作为一种实际应用,利用该参数提取了目标与金属球、二面角等典型目标的散射相似性参数,并指出:球面散射相似性、二面角散射相似性和体散射相似性具有双重物理内涵,既表征了目标与典型目标散射相似程度,又表征了这些典型散射对目标后向散射贡献率。着重分析了球面散射相似性独特性质,及与极化散射熵、平均Alpha角之间的数学约束关系。这些研究为后续散射分类应用提供了理论支持。该部分研究成果已连续被《IEEE Transaction on Geoscience and Remote Sensing Letters》、《信号处理》、《电子学报》等刊用;
     7)在分析现有散射随机性度量参数的基础上,得出“极化散射熵是目前度量目标散射随机性最合理的参数”这一结论。针对极化散射熵存在不足,着重开展了两个层次的研究:其一,鉴于极化散射熵采用相干矩阵特征值定义,在分析相干矩阵不变特征量基上,定义了基于2-范数的极化散射熵替代参数,并分析了它与极化散射熵之间的数学约束关系;其二,考虑到散射回波极化度也包含了散射随机性信息,定义了一种基于极化度的散射随机性度量新参数。结合典型粒子云团,从理论上阐明了新参数度量目标散射随机性的合理性。实测数据实验结果表明,上述参数克服了极化散射熵的运算量偏大问题。该方面的研究成果部分已被《信号处理》刊用;
     8)对POLSAR图像相干斑抑制技术进行了全面地研究,指出了现有类多视平均处理一类相干斑抑制算法在运算量、目标信息保持等方面的不足,并提出了一种基于像素筛选的POLSAR图像相干斑抑制新算法。该算法避免了现有相干斑抑制技术无法同时兼顾相干斑抑制和目标极化、边缘等信息保持的局限,能够自动地筛选同主散射机制、均匀区像素参与最小线性均放误差滤波处理,为POLSAR图像相干斑抑制提供了新的思路。该方面研究成果已被《信号处理》刊用。
     9)针对现有散射分类存在的不足,从两个层次开展了研究:其一,提出了一种基于球面散射相似性和极化散射熵替代参数的H/Alpha替代分类方法,克服了H/Alpha散射分类运算量偏大的问题,但仍存在类别边界人工选取的问题;其二,提出了一种基于多散射相似性参数和散射随机性的散射分类新方法。该方法采用散射随机性将目标分为高、中、低散射随机性三大类,然后利用球面散射相似性、二面角散射相似性和体散射相似性提取目标散射机制鉴别表征目标散射,即对于低散射随机性目标,采用散射相似性最大对应的典型散射表征目标散射;对于中散射随机性目标,采用散射相似性较大对应的典型散射联合表征目标散射;对于低散射随机性目标,不进一步细分。为POLSAR图像散射分类提供了新的思路和途径;该方面研究的部分成果已被《信号处理》、《电子学报》等刊用,部分成果已投国际权威杂志《IEEE Transaction on Geoscience and Remote Sensing》;
     10)针对Wishart距离度量运算量偏大的不足,提出了一种度量两类目标散射差异性的新参数,分析了目标SCR变化对该参数影响,进而阐明利用目标特征极化理论提高目标SCR的重要性。以此为基础,又提出了一种POLSAR图像地物无监督迭代分类新方法。即将散射分类结果作为初始类别,利用新参数进行类别迭代调整,取得了较好的实验效果。该方面部分研究成果已被《电子学报》等刊用,部分成果已投国际权威杂志《IEEE Transaction on Geoscience and Remote Sensing》;
As one basic feature of electromagnetic wave, the utilization of radar polarization has the advantages of improving radar detection and recognition ability. Thanks to advances in POLSAR systems and information technology over the past decade,radar polarimetry has become a subject of recurring and globally intensifying interest all around world. However, the basic theories of radar polarimetry are still not perfect, which blocks its applicactions. With such a researching background, this thesis comprehensively investigates multi basic theories of radar polarimetry, namely target characteristic polarization theory and target scattering modeling. Some new theories and techniques are proposed, and their joint applications in improving terrain classification of polarimetric SAR(POLSAR) image are also systematically studied by theoretic analysis and experimental validation with lots of real high-resolution POLSAR data.
     Our research follows the ordinary process: first, existing work is thoroughly reviewed; second, based on related research and problems, researching points of this thesis are determined and further research is carried out. The work of this thesis can be concluded as follows.
     1) Target characteristic polarization theory for the coherent case is studied in greater detail, which develop target characteristic polarization theory ulteriorly. Power extrema and their conditions are derived firstly. And then the behavior of target characteristic polarization states as functions of target parameters are discussed for the first time and important conlusions are gived, which can be used to decrease computational complexity of target characteristic polarization states and anticipat their positions on the power density plot. Some of this studies have been published in some Chinese journals such as Signal Processing, cience in China Series E: Technological Science, etc.
     2) An improven Lagrange multiplier method is proposed to find the optimal polarizations for the co-polarized incoherent case. The traditional Lagrange multiplier method requires solving a sixth-order polynomial equation of the Lagrange multiplier. However, since it is unknown which of the roots corresponds to the maximum received power and which corresponds to the minimum, all the real roots need to be found, which will cause heavy computational burden. Thus two aspects are studied in thesis. First, We have proved for the first time that the largest and smallest roots of the Lagrange multiplier equation correspond to the maximum and minimum received powers, respectively. Second, the minimum search intervals of these two roots are also given by some theoretical analysises. Based on these researches, the flow of the proposed procedure is presented, which takes at least one to three times less computational time than the original procedure. The achievement of this aspect has been accepted to publish by“Signal Processing”and IEEE Transactions on Geoscience and Remote Sensing Letter.
     3) A fast algorithm is proposed to find the optimal polarizations for arbitrary polarized channel. Firstly, the function of received power in arbitrary channel is unified as the form of copolar power. Then based on the change of polarimetric basis, the copolar power is analyzed theoretically to obtain the minimum interval of target optimal polarization state, which provides theoretic support for simpling the process of obtaining optimal polarization states or anticipating their positions. Finally, the interval dichotomy is used to search in the minimum interval of target optimal polarization state. The experiment results have demonstrated the validity of the proposed algorithm. The achievement of this aspect has been accepted to publish by“acta geodaetica et cartographica sinica”.
     4) A three step procedure is proposed to find the optimal polarizations of random targets for the case of no restriction between receiving and transimitting antenna. Considering that the scattering waves of random targets are partially polarized, the optimal receiving is firstly discussed for partially polarized wave, and the mismatched/matched receiving conditions and their corresponding receiving powers are obtained. Then the flow of the proposed three procedure is given: a) the optimal transmitted polarization states is obtained by optimizing mismatched/matched receiving power; b) the optimal scattering wave can be derived by substituting the optimal transmitted polarization states into the scattering equation; c) the optimal received polarization states can be obtained by the mismatched/matched receiving conditions for the incoherent case. The experiment results have demonstrated the practicability of the proposed procedure. The achievement of this aspect has been accepted to publish by“Signal Processing”.
     5) after thoroughly reviewing existing works, we conclude that the scattering model based H/Alpha is the most integrated one of target scattering now. But through some theoretical analysis of polarimetric scattering entropy, we find that this scattering model is not reasonable for some cases because it sets out with the assumption that there is always a dominant“average”scattering mechanism, which doesn’t always come into existence. And the more reasonable model is to represent target scattering with different scattering mechanism according to target scattering randomness. That is to say, the main scattering mechanism is used for low scattering randomness; the main and minor scattering mechanism are adopted together for median scattering randomness. The achievement of this aspect has been submitted to IEEE Transactions on Geoscience and Remote Sensing.
     6) Aiming at the deficiency of the similarity parameter to measure the degree of scattering similarity between distributed targets and canonical targets, a new parameter, namely polarimetric scattering similarity, is proposed based on the Pauli vector of a canonical target and the coherency matrix of a distributed target firstly. And the existing similarity parameter is just a special case of the proposed parameter. As an application, the parameters to several canonical targets, eg. metal sphere are derived and their physical meanings or properties are discussed. Finally, the relationship between the surface scattering similarity and average apha angle or polarimetry scattering entropy is also analysized. The achievement of this aspect has been accepted to publish by“Signal Processing”,“Acta Electronica Ainica”and IEEE Transactions on Geoscience and Remote Sensing Letter.
     7) Aiming at the deficiency of the present parameters to measure target scattering randomness, a novel parameter is defined based on the degree of polarization of target scattering wave. Firstly, after the analysis of the relationship between the degree of polarization of target scattering wave and target scattering randomness, the novel parameter is constructed with the degree of polarization of target scattering wave. And then the feasibility of the novel parameter to measure target scattering randomness is clarified by the analysis of the behavior of the novel parameter as a function of the scattering order in volume backscatter or target anisotropy. As the new parameter involves simple operation, it overcomes the time-consuming operation of the present parameters. The achievement of this aspect has been accepted to publish by“Signal Processing”.
     8) A fast algorithm based on the automatic censoring is proposed to filter the speckle in POLSAR image. Firstly, a rectangle widow is sliding through each pixel of POLSAR imagery. For each current pixel, a global threshold is used to determine whether the current pixel is a point target. If not, then the homogeneous pixels of the same main scattering mechanism with this current pixel are censored automatically in the current sliding-window. Finally, the locally linear minimum mean-squared error (LLMMSE) estimator is used to filter this pixel with the selected pixels. The selection of homogeneous pixels based on the ML texture reduces the effect of speckle; the selection of the same main scattering mechanism pixels based on scattering similarity preserves the main scattering mechanism. The experiment results show that the proposed algorithm has the well theoretical and actual performance in speckle filtering, texture preservation and target main scattering preservation. The achievement of this aspect has been accepted to publish by“Signal Processing”.
     9) Aiming at the deficiencies of the H/Alpha based classification, an alternative to H/Alpha based classification is proposed by replacing alpha angle with surface scattering similarity. But it still has the deficiency of arbitrarily fixed linear decision boundaries. Thus, a new scheme of unsupervised terrain classification is proposed based on multi scattering similarities. This new scheme divides terrain scattering into three classes with target scattering randomness firstly; and subdivides three classes into ten classes with surface scattering similarity, double scattering similarity and volume scattering similarity ulteriorly. As terrain scattering class is determined by target scattering similarities automatically, the deficiency of the arbitrarily fixed linear decision boundaries of Alpha angle is overcomed. Surface scattering, double scattering and volume scattering are the inherent characteristics of terrain physical scattering mechanisms, the classification result of the new method is consistent with the real terrain scattering. The achievement of this aspect has been accepted to publish by“Signal Processing”and“Acta Electronica Ainica”.
     10) Aiming at the deficiencies of wishart classifier, a new parameter is proposed to measure target scattering difference first, and then applied to refine the result of the above proposed scattering classification. And good performance is avcheived for POLSAR image terrain classification. The achievement of this aspect has been accepted to publish by“Acta Electronica Ainica”.
引文
[1]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.
    [2]王雪松.宽带极化信息处理的研究[D].长沙:国防科技大学, 1999.
    [3] Sinclair G. The transmission and reception of elliptically polarized radar waves[J] .Proceedings of the IRE, Feb.1950, 38: 148-151.
    [4] Lee J S, Pottier E. Polarimetric radar imaging from basics to applications[M]. 2009.
    [5] Boerner W M. Direct and inverse methods in radar polarimetry[M]. Kluwer Academic Publishers, Netherlands, 1992.
    [6] Mott H. Remote sensing with polarimetric radar[M]. IEEE press, 2007.
    [7] van Zyl J J, Zebker H A, Elachi C. Imaging radar polarization signatures: theory and observation[J]. Radio Science, 1987, 22(4): 529-543.
    [8] Zebker H A, van Zyl J J. Imaging radar polarimetry: a review[J]. Proceedings of the IEEE, 1991, 79(11): 1583-1606.
    [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] 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.
    [11] Boerner W-M. Basics of SAR polarimetry I[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
    [12] Boerner W-M. Basics of SAR polarimetry II[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2007.
    [13] 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.
    [14] Boerner W-M. Recent advances in radar polarimetry and polarimetric SAR interferometry[R]. Neuilly-sur-Seine, France: Research and Technology Organisation (NATO), 2004.
    [15] van Zyl J J. On the importance of polarization in radar scattering problems[D]. Pasadena, CA, USA: California Institute of Technology, 1985.
    [16] Agrawal A P. A polarimetric rain backscatter model developed for coherent polarization diversity radar applications[D]. Chicago, IL, USA: University of Illinois, 1986.
    [17] Yang J. On theoretical problems in radar polarimetry[D]. Niigata-shi, Japan: NiigataUniversity, 1999.
    [18]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.
    [19]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.
    [20] Kennaugh E M. Polarization properties of radar reflections [D]. M.S. thesis, The Ohio State University, Columbus, Ohio, 1952.
    [21] Huynen J R. Phenomenological theory of radar targets[D]. Delft, The Netherlands: Technical University of Delft, 1970.
    [22] Deschamps G A. Geometrical representation of the polarization state of a plane EM wave [J]. Proceeding of the IRE, 1951, 39: 540~544.
    [23] Gent H. Elliptically polarized waves and their reflections from radar targets: a theoretical analysis. Telecommunications Research Establishment, Chelenham, England, UK: TRE-MEMO 584. March 1954.
    [24] Copeland J D. Radar target classification by polarization properties. Proceeding of the IRE, 1960, 48:1290~1296.
    [25] ESA. Input data sources: airborne missions[EB/OL]. http://earth.esa.int/polsarpro /input.html, 2006-12-20.
    [26] ESA. Input data sources: spacaeborne missions[EB/OL]. http://earth.esa.int/ polsarpro/input_space.html, 2006-12-20.
    [27] 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.
    [28] 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.
    [29] 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.
    [30] 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. [30] 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.
    [31] 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.
    [32] 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.
    [33] 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.
    [34] 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.
    [35] 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.
    [36] 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.
    [37] 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.
    [38] 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.
    [39] 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.
    [40] 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.
    [41] Mieras H. Optimum polarizations of simple compound targets [J]. IEEE Trans. Antennas propagate, 1983, Ap-31(11):996-999.
    [42] Kostinski A B, ect. On foundations of radar polarimetry[J]. IEEE Antennas propagate., Ap-34:1395-1404, 1986.
    [43] 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.
    [44] Xi An-Qing, ect. Determination of the characteristic polarization states of the target scattering matrix for the coherent monostatic and reciprocal propagation space. SPIE Polarimetry: radar, infrared, visible, ultraviolet and X-ray, 1990, 1317: 166-190.
    [45] Boerner W-M, et al. On the basic principles of radar polarimetry: the target characteristic 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.
    [46] Yamaguchi Y, ect. On characteristic polarization states in the cross-polarized radar channel. IEEE Trans. Geosci. Remote Sens., 1992, 30(5):1078-1081.
    [47] Yan W L, ect. Optimal polarization states determination of the Stokes reflection matrices for the coherent case, and of the Mueller matrix for the partially polarized case in Direct and inverse methods in radar polarimetry(Part 1)(Kluwer academic publishers, The Netherlands, 1992), 351-385.
    [48] Yang J, ect. The formulae of the characteristic polarization states in the co-pol channel and the optimal polarization state for contrast enhancement. IEICE Trans. Commun. 1997, E80-B: 1570-1575.
    [49] Yang J, ect. Simple method for obtaining characteristic polarization states. Electronics Letters, 1998, 34(5): 441-442.
    [50] Davidovitz M, etc. Extension of Kennaugh’s optimal polarization concept to the asymmetric matrix case. IEEE. Trans. Antennas Propag., 1986, 34(4):569-574.
    [51] Chu C M. Optimal polarization in bistatic scattering. IEEE, 1988, 30(3): 530-532.
    [52] Lin S M. Elgenvalue problem and Kennaugh’s optimal polarization for the asymmetric scattering matrix case. IEEE, 1990, 25(4): 562-565.
    [53] Germond A L, ect. Bistatic radar polarimetry theory. In Ultra-wideband radar edited by Taylor J D, 2001.
    [54] Van Zyl, etc. On the optimum polarizations of incoherently reflected waves. IEEE Antennas Propag. , 1987 35(7):818-825.
    [55] Kostinski A B, ect. Optimal reception of partially polarized waves. J. Opt. Soc Am.A, 1988, 5:58-64.
    [56] Tragl K. Polarimetric radar backscattering from reciprocal random targets. IEEE Trans. Geosci. Remote Sens. , 1990 28(5):856-864.
    [57] Tragl K, ect. A polarimetric covariance matrix concept for random radar targets. In Preprints of the International Conference on Antennas and Propagation, 1991, IEE Pul.333: 396-399.
    [58] Kingsbury J, ect. Radar-partially polarized backscatter description algorithms and applications. IGARSS92, 1992, 74-76.
    [59] Ziegier V, ect. Mean backscattering properties of random radar targets: a polarimetric covariance concept. IEEE, 1992, 266-288.
    [60] Lee. J K, ect. Optimum polarizations in the bistatic scattering from layered random media. IEEE Trans. Geosci. Remote Sens., 1994, 32(1):169-175.C
    [61] McCormick G. The theory of polarization diversity systems: the partially polarized case. IEEE Trans. On Ant. And Prop., 1996, 44:425-433.
    [62] Hubbert J C, A comparision of radar, optic and specular null polarization theories. IEEE Trans. Geosci. Remote Sensing,1994, 32(3):658-671.
    [63] Yang J. Kennaugh’s optimal polarization for the multistatic radar. IEEE AP-S, 1992, 2:842-844.
    [64] Titin-Schnaider C, Power optimization for polarimetric bistatic random mechanisms [J], IEEE Trans. Geosci. Remote Sens., 2007, 45: 3646-3660.
    [65] Ioannidis G A, ect. Optimum antenna polarizations for target discrimination in clutter. IEEE Trans. Antenna Propagat, 1979, AP-27: 357-257.
    [66] Kostinski A B, ect. On the polarimetric contrast optimization. IEEE trans. Antennas and Propagation, 1987, AP-35(8): 989-991.
    [67] Tanaka M. Polarimetric contrast optimization for partially polarized waves. IEEE, 1989.
    [68] Yang Jian, ect. On the problem of the polarimetric contrast optimization. IEEE, 1990.
    [69] Touzi R G S, ect. Assessment of polarimetric contrast optimization techniques for completely polarized waves. IEEE, 1991.
    [70] Tanaka M, ect. Optimum antenna polarizations for polarimetric contrast enhancement. In proc. 1992 int. Symp. Antennas and Propagation, Sapporo, Japan 1992, 2: 545-548.
    [71] Verbout S M, ect. Polarimetric techniques for enhancing SAR imagery. SPIE Vol.1630 Synthetic Aperture Radar, 1992.
    [72] Santalla V, ect. A method for polarimetric contrast optimization in the coherent case. IEEE, 1993.
    [73] Optimum antenna polarizations for target discrimination in clutter, Northwestern Polytechnical Univ, Xi'an, China,1994.
    [74] Yamaguchi Y, ect. Polarimetric enhancement of Pol-SAR imagery applied to JPL-airsar polarimetric image data, international Geoscience and Remote Sensing Symposium (IGARSS), 1995.
    [75] Mott H, ect. Polarimetric contrast enhancement coefficients for perfecting high resolution POL-SAR/SALK image feature extraction. In SPIE, Wideband Interferometric Sensing and Imaging Polarimetry, 1997, 3120:106-117.
    [76] Yang Jian, ect. Numerical methods for solving the optimal problem of contrast enhancement. IEEE trans. Geoscience and remote sensing, 2000, 38(2):965-971.
    [77] Yang J, Peng Y N, Lin S M. Similarity between two scattering matrices[J]. Electro. Lett., 2001,37(3):193-194.
    [78] Yang J, ect. Generalized optimization of polarimetric contrast enhancement. IEEE Trans. Geoscience and remote sensing letters, 2004.
    [79]杨健等.相对最优极化的最新进展.遥感技术与应用,2005, 20(1): 38-41.
    [80]余海坤等.极化SAR目标相对最优极化研究.雷达科学与技术, 2006, 4(5): 297-300.
    [81] Sarabandi K, ect. Characterization of optimum polarization for multiple target discrimination using genetic algorithms. IEEE Trans. Antennas and Propagation, 1997, 45(12): 1810-1817.
    [82] Stapor D P. Optimal receive antenna polarization in t he presence of interference and noise. IEEE Trans Antennas and Propagation , 1995 , 43 (5) :473 -477.
    [83]王雪松,庄钊文,肖顺平,等.极化信号的优化接收理论:完全极化情形.电子学报, 1998 , 26 (6) :42 - 46.
    [84]王雪松,徐振海,代大海,等.干扰环境中部分极化信号的最佳滤波.电子与信息学报, 2004, 26 (4): 593 - 597.
    [85]王雪松,肖顺平,陈志杰,等.部分极化情况下SINR极化滤波器性能研究.应用科学学报, 1999, 17 (2) : 177 - 182.
    [86]王雪松,代大海,徐振海,等.极化滤波器的性能评估与选择.自然科学进展, 2004 , 14 (4) : 442 -448.
    [87]徐振海,王雪松,施龙飞,等.信号最优极化滤波及性能分析.电子与信息学报, 2006 , 28 (3) : 498-501.
    [88] Yang Y F, Tao R, Wang Y. A new SINR equation based on the polarization ellipse parameters. IEEE Trans Antennas and Propagation, 2005, 53 (4): 1571-1577.
    [89] 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.
    [90] 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.
    [91] 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.
    [92] 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.
    [93] 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.
    [94] 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.
    [95] 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.
    [96] 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.
    [97] 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.
    [98] 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.
    [99] Yueh H A, et al. Bayes classification of terrain cover using normalized polarimetric data[J]. Journal of Geophysical Research, 1988, 93(B12): 15261-15267.
    [100] 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.
    [101] 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.
    [102] 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.
    [103] 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.
    [104] 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.
    [105] 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.
    [106] Keshava N, Moura J M F. Matching wavelet packets to Gaussian random processes[J]. IEEE Transactions on Signal Processing, 1999, 47(6): 1604-1614.
    [107] 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.
    [108] 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.
    [109] Pellizzeri T M, et al. Multitemporal/multiband SAR classification of urban areasusing spatial analysis: statistical versus neural kernel-based approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2338-2353.
    [110] Lombardo P, et al. Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 1959-1975.
    [111] 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.
    [112] 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.
    [113] 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.
    [114] Rignot E, Chellappa R. Segmentation of polarimetric synthetic aperture radar data[J]. IEEE Transactions on Image Processing, 1992, 1(3): 281-300.
    [115] 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.
    [116] 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.
    [117] 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.
    [118] 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.
    [119] 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.
    [120] 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.
    [121] 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.
    [122] 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.
    [123] Kersten P R, Lee J S, Ainsworth T L. Unsupervised classification of polarimetricsynthetic aperture radar images using fuzzy clustering and EM clustering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 519-527.
    [124] 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.
    [125] Trizna D B, et al. Projection pursuit classification of multiband polarimetric SAR land images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2380-2386.
    [126] 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.
    [127] 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.
    [128] 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.
    [129] Alberga V. Comparison of polarimetric methods in image classification and SAR interferometry applications[D]. Chemnitz, Saxony, Germany: Technical University of Chemnitz, 2004.
    [130] Kimura K. A study on target classification/detection in polarimetric SAR image data[D]. Niigata-shi, Japan: Niigata University, 2005.
    [131] 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.
    [132] 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.
    [133] 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.
    [134] 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.
    [135] 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.
    [136] Pottier E, Cloude S R. Unsupervised classification of full polarimetric SAR data and feature vectors identification using radar target decomposition theorems andentropy analysis[A]. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS’95)[C], Florence, Italy, 1995: 2247-2249.
    [137] 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.
    [138] 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.
    [139] 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.
    [140] 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.
    [141] 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.
    [142] 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.
    [143] 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.
    [144] 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.
    [145] 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.
    [146] 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.
    [147] 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.
    [148]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.
    [149] 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.
    [150] 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.
    [151] 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.
    [152] 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.
    [153] 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.
    [154] 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.
    [155] Fleischman J G, et al. Multichannel whitening of SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(1): 156-166.
    [156] 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.
    [157] 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.
    [158] Lee J S, Grunes M R, De Grandi G. Polarimetric SAR speckle filtering and its implication for classfication[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2363-2373.
    [159] 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.
    [160] 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.
    [161] Lopez-Martinez C, Fabregas X. Polarimetric SAR speckle noise model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2232-2242.
    [162] 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.
    [163] 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.
    [164] Lopez-Martinez C. Multidimensional speckle noise, modelling and filtering related to SAR data[D]. Barcelona, Spain: Technical University of Catalonia, 2003.
    [165] 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.
    [166] 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.
    [167] 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.
    [168] 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
    [169] Fleischman J G, et al. Multichannel whitening of SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(1): 156-166.
    [170] 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.
    [171] 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.
    [172] Lee J S, Grunes M R, De Grandi G. Polarimetric SAR speckle filtering and its implication for classfication[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2363-2373.
    [173] 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.
    [174] 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.
    [175] Lopez-Martinez C, Fabregas X. Polarimetric SAR speckle noise model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2232-2242.
    [176] 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.
    [177] 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.
    [178] Lopez-Martinez C. Multidimensional speckle noise, modelling and filtering related to SAR data[D]. Barcelona, Spain: Technical University of Catalonia, 2003.
    [179] 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.
    [180] 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.
    [181] 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.
    [182] 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.
    [183] 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.
    [184] 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.
    [185] Schou J, et al. CFAR edge detector for polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1): 20-32.
    [186] Touzi R, et al. Ship detection and characterization using polarimetric SAR[J]. Canadian Journal of Remote Sensing, 2004, 30(3): 552-559.
    [187] 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.
    [188] 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.
    [189] 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.
    [190] 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.
    [191] Schuler D L, Lee J S, De Grandi G. Measurement of topography using polarimetricSAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(5): 1266-1277.
    [192] Schuler D L, et al. Terrain topography measurement using multipass polarimetric synthetic aperture radar data[J]. Radio Science, 2000, 35(3): 813-832.
    [193]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.
    [194]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.
    [195] 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.
    [196] Hajnsek I. Inversion of surface parameters using polarimetric SAR[D]. Jena, Germany: Friedrich-Schiller University Jena, 2001.
    [197] Lopez-Sanchez J M. Analysis and estimation of biophysical parameters of vegetation by radar polarimetry[D]. Valencia, Spain: Universidad Politecnica de Valencia, 1999.
    [198] 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.
    [199] Reigber A. Airborne polarimetric SAR tomography[D]. Stuttgart, Germany: University of Stuttgart, 2001.
    [200] 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.
    [201] Cloude S R, Papathanassiou K P. Polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1551-1565.
    [202] Papathanassiou K P, Cloude S R. Single-baseline polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2352-2363.
    [203]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.
    [204]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.
    [205] Mette T, Papathanassiou K P, Hajnsek I, and Zimmermann R. Forest biomass estimation using polarimetric SAR interferometry. 2002 IEEE International Geoscience and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, June 24-28, 2002,Tornoto Canada. IGARSS '2002, (2): 817-819.
    [206] Cloude S R. Robust parameter estimation using dual baseline polarimetric SAR interferometry. 2002 IEEE International Geoscience and Remote SensingSymposium and the 24th Canadian Symposium on Remote Sensing, June 24-28, 2002,Tornoto Canada. IGARSS '2002, (2): 838-840.
    [207] Luneburg E. Principles of radar polarimetry[J]. IEICE Transactions on Electronics (Special Issue on Electromagnetic Theory), 1995, E78-C(10): 1339-1345.
    [208] Kostinski A B, Boerner W-M. On foundations of radar polarimetry[J]. IEEE Transactions on Antennas Propagation, 1986, AP-34(12): 1395-1404.
    [209] Guissard A. Mueller and Kennaugh matrices in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3): 590-597.
    [210] Takagi T. On an algebraic problem related to an analytical theorem of Caratheodory and Fejer and on an allied theorem of Landau. Japanese J Math. 1927, 1:83-93.
    [211] Park S E, Moon W M. Classification of the Polarimetric SAR using fuzzy boundaries in entropy and alpha plane[C]. Procceding of IEEE, 2005, 5517-5519.
    [212] Lee J S, Ainsworth T L, Kelly J P, Martinez C L.Evaluation and Bias Removal of Multilook Effect on Entropy/Alpha/Anisotropy in Polarimetric SAR Decomposition[J].IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46(10): 3039-3052.

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