【作者】 周晓光；

【导师】 万建伟； 匡纲要；

【作者基本信息】 国防科学技术大学 ， 信息与通信工程， 2008， 博士

【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。更多还原

【Abstract】 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.更多还原