基于局部竞争策略的极化SAR图像精细分类

合成孔径雷达（Synthetic Aperture Radar, SAR）成像技术已经成为一种高分辨对地观测的重要手段之一,而极化SAR图像地物分类一直是其中的研究热点。基于复Wishart分布的最大似然（Maximum Likelihood,ML）分类器是最经典的极化SAR图像分类算法之一,但由于地物类型的复杂性、区域的不均匀性等原因使得基于像素的ML-Wishart分类器的分类精度不高。针对这个问题,本文提出了一种基于复Wishart分布的局部最大后验概率（Maximum a Posteriori,MAP）竞争方法,该算法通过计算伪先验概率,并在每个像素的局部窗口中实施MAP分类器,可以提高复杂区域图像的分类精度。该文主要研究了4种基于Wishart分布的分类算法,包括经典复Wishart分类算法、混合复Wishart模型、基于马尔科夫随机场（Markov Random Field, MRF）的混合复Wishart模型和基于局部竞争策略的MAP分类算法。在混合模型建模中,不同于以往的对整幅图像进行建模的模型策略,本文采用对单个类别进行混合建模的策略。实验对比分析了上述4个分类器和SVM分类器在C波段RADARSAT-2多时相的全极化SAR农田数据上的分类效果。实验结果表明,所提出的基于局部竞争策略的分类器对数据的分类结果稳定,具有最高的分类精度,基于混合Wishart的MRF模型分类结果次之。     

引入有取向二面角散射的Freeman-Durden 分解

该文首先考察了当极化SAR 方位向与农作物种植行向不一致时,入射电磁波到地表、农作物的二次散射与一般二次散射的区别。其次,为描述这种二次散射,建立了有取向的二面角散射模型,并将该模型引入到Freeman-Durden 目标分解中,设计了相应的目标分解算法。最后,选取同一农作物种植区两种航迹的机载全极化SAR 数据实现了该分解算法。实验结果证明,对于农作物种植区,改进后的Freeman-Durden 分解能提升不同航迹下的极化SAR 数据目标分解的一致性。      

基于复Wishart分布的极化SAR图像无监督分类

文中使用了一种无监督算法对全极化合成孔径雷达数据进行地物分类。不同于其他算法对像素统计特性分类而忽略他们的散射特性,这种算法不仅使用了统计分类,而且还保留了其主要的散射特性。本算法采用的是由Freeman和Durden以散射模型为基础开发的分解算法和基于复Wishart分布的距离度量分类器的组合。首先是应用Freeman和Durden分解划分像素分成三个散射类:表面散射,体散射和二面角散射;然后在此基础上将这三个散射类分为多个聚类,通过迭代Wishart分类器将其更精准地分类;最后,根据散射类别的自然颜色对其进行像素编码,提出颜色填充的方案。通过实验结果比对来证明该方法比H/α方法更具有优越性。     

加权全极化SAR图像非监督Wishart分类方法

为提高基于极化目标分解与复Wishart非监督分类方法中对不同类别地物中心散射相关矩阵的估值精度与合理性,本文提出了加权全极化SAR图像非监督Wishart分类方法,该方法通过对求解每一类地物散射相关矩阵时,进行数值加权,使得求解的散射相关矩阵更能代表地物类别的中心。本文详细阐述了该方法的原理和实施步骤,并通过对AIRSAR的L波段实际数据进行分类实验,可知该加权算法无论在分类精确度上还是在迭代速度上,性能都有所提高。     

PolSAR噪声抑制对Freeman-Durden分解的影响分析

噪声抑制的目的之一是减小由噪声引起PolSAR数据极化分解误差。首先利用仿真Po-lSAR数据分析了在噪声抑制过程中典型地物和混合地物的Freeman-Durden分解误差与多视处理程度的关系,给出了不同地物Freeman-Durden分解参数的无偏估计所需的多视处理独立样本数;进一步利用实测ESAR数据分析了不同噪声抑制算法对Freeman-Durden分解参数的影响,实现了图像应用层面的PolSAR噪声抑制效果评估。     

采用流形近邻传播聚类的极化SAR图像分类

针对传统近邻传播（Affinity Propagation, AP）聚类算法使用欧式距离构建相似度矩阵,不能有效描述极化SAR数据复杂分布的问题,本文提出一种新的基于联合流形距离的AP聚类算法(CMD-AP) 用于极化SAR图像分类。首先将待分类极化SAR图像分割成若干超像素,在相应的极化特征基础上加入图像纹理特征,利用拉普拉斯特征映射算法对特征降维;然后结合相干矩阵Wishart流形和特征矢量欧式流形作为流形距离测度,构造相似性矩阵;最后利用上述相似性矩阵,采用AP聚类算法,对极化SAR图像进行分类。该算法充分考虑了极化SAR数据集潜在的流形结构,将联合的流形距离测度引入AP算法中。实验表明,本文算法提高了极化SAR图像的分类精度,具有更优的区域一致性和边缘保持效果。      

基于去取向理论的全极化SAR图像模糊非监督聚类

由于复杂散射体的随机取向导致其回波具有一定的波动性,利用目标分解理论对全极化SAR图像进行分类时,分类结果会出现一定程度的错分现象。该文提出了一种新的非监督分类算法,该算法首先根据去取向理论,将目标向量旋转到最小交叉极化方向;然后,采用u/v/H参数描述散射机制,以模糊隶属函数代替参数平面的硬阈值划分;最后,以多元复Wishart分布描述相干矩阵,基于Bayes极大似然分类准则进行分类。以中国广东淡水附近的L波段NASA/JPL SIR-C全极化SAR图像作为实验数据进行了仿真试验,并进一步对聚类中心的迁移进行了讨论。试验和讨论结果表明:同基于H/和类k-mean的算法比较,该文的聚类算法对聚类效果有明显改善,类别对应的散射机制也更为准确,分类结果有利于地表类型的自动识别。     

SAR多视图像的Stokes参数的统计分布

该文首先应用散射矩阵的Wishart 联合分布推导了高斯分布散射矩阵的Stokes参数分布,利用极化不变量思想得到了全部4个Stokes参数分布相当理想的理论结果,并与前人的结果进行了对比,体现了此分析算法的简洁性与统一性,他们可以具体描述Stokes参数的起伏性。在此基础上,求出了SAR多视情况下标准化Stokes参数分布的概率密度函数,从另一方面描述了SAR多视图像的随机状态特性,这些结果对于降低斑点噪声、地表分类与背景中的目标识别具有重要的指导意义。     

基于城区特征和SLIC的简缩极化SAR分类方法

简缩极化SAR作为一种特殊的双极化模式,可以获取较为全面的极化信息,同时也能获得较大的成像幅宽,近年来得到了研究人员的关注。但以往基于极化度的分解方法存在体散射过估计的问题,导致分解与分类的结果在城区部分,尤其是大方位角城区部分表现一般。本文采用基于城区描述子的简缩极化分解方法,将分解获取的特征进行Wishart迭代分类,同时利用SLIC算法进行超像素分割,在超像素区域内进行类别合并,从而改善分类效果。实验采用Radarsat-2旧金山区域的全极化数据仿真合成CTLR模式及π/4模式的简缩极化数据验证了算法的可行性,实验表明,对于两种模式,本文方法在小方位角城区分类精度提高约20%,大方位角城区分类精度提高约10%。     

基于Cloude-Pottier目标分解和聚合的层次聚类算法的全极化SAR数据的非监督分类算法研究

本文提出了一种新的基于Cloude-Pottier分解和聚合的层次聚类的全极化SAR (Synthetic Aperture Radar)数据的非监督分类算法.作者使用极化总功率SPAN来改进常规的初始化方法,并采用聚合的层次聚类算法对初始化结果进行类的合并,提高非监督分类器的性能.实验表明,该算法能获得有效的分类中心,分类结果明显优于常规的Wishart H/α/A分类算法.     

Unsupervised terrain classification preserving polarimetric scattering characteristics

In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum-likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images.     

Unsupervised PolInSAR classification based on optimal coherence set

Aiming to solve the misclassification problems of unsupervised polarimetric Wishart classification algorithm based on Freeman decomposition, an unsupervised Polarimetric Synthetic Aperture Radar (SAR) Interferomery (PolInSAR) classification algorithm based on optimal coherence set parameters is studied and proposed. This algorithm uses the result of Freeman decomposition to divide the image into three basic categories including surface scattering, volume scattering, and double-bounce. Then, the PolInSAR optimal coherence set parameters are used to finely divide each of the three basic categories into 9 categories, and the whole image is divided into 27 categories. Because both the Freeman decomposition result and optimal coherence set parameters indicate specific scattering characteristics, the whole image is merged into 16 categories based on physical meaning. At last, the Wishart cluster is employed to obtain the final classification result. To preserve the purity of scattering characteristics, pixels with similar scattering characteristics are restricted to be classified with other pixels. The final classification results effectively resolve the misclassification problem, not only the buildings can be effectively distinguished from vegetation in urban areas, but also the road is well distinguished from grass. In this paper, the E-SAR PolInSAR data of German Aerospace Center (DLR), are used to verify the effectiveness of the algorithm.     

基于U分布的PolSAR图像无监督MAP分类方法

结合U分布对不同匀质性极化合成孔径雷达(PolSAR)数据的广泛建模能力及Potts马尔科夫随机场(MRF)模型对像素点之间类相关性的建模能力,提出了一种基于最大后验概率(MAP)准则的PolSAR图像无监督分类方法。利用迭代条件模式算法和Metropolis采样算法对像素点的类别进行更新,迭代过程中分布参数的估计采用基于梅林(Mellin)变换的矩阵对数累积量方法,以迭代过程中出现次数最多的类别最为像素点的最终分类结果。利用NASA/JPL实验室AIRSAR系统获取旧金山湾的PolSAR数据,对本文分类算法的有效性以及分布的杂波建模能力进行了仿真验证。实验结果表明,本文分类算法的精度优于Lee分类算法,分布对PolSAR数据的杂波建模准确性总体上优于复Wishart分布、K分布和G0分布。     

Unsupervised classification of multifrequency and fullypolarimetric SAR images based on the H/A/Alpha-Wishart classifier

Introduces a new classification scheme for dual frequency polarimetric SAR data sets. A (6×6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6×6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR sensor in 1989     

散射模型与Wishart分类相结合的极化SAR图像分类

给出了一种散射模型与Wishart分类相结合的极化合成孔径雷达(polarimetric synthetic aperture radar, PolSAR)图像非监督分类方法。首先利用去取向三分量散射模型进行粗分类,将像素划分为三种基本散射类型和混合散射类型;然后,在基本散射类型内根据占优散射机制的功率进行细分类,并根据Wishart距离对细分类的结果进行类别合并,合并到指定的类别数;最后对四种散射类型的像素分别重新进行Wishart迭代,从而实现极化SAR数据的非监督分类。利用美国AIRSAR机载系统采集的实测数据进行实验,并且同已有分类方法进行比较,结果表明本文方法改善了分类效果,且降低了体散射过估计。      

Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering

Five clustering techniques are compared by classifying a polarimetric synthetic aperture radar image. The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl lscr//sub 2/ metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the /spl lscr//sub p/ norms. The results support the conclusion that the pixel model is more important than the clustering mechanism.     

基于半监督学习的SVM-Wishart极化SAR图像分类方法

该文针对极化SAR (Synthetic Aperture Radar)图像分类中的小样本问题,提出了一种新的半监督分类算法。考虑到极化SAR数据反映了地物的散射特性,该方法首先利用目标分解方法提取了多种极化散射特征;其次,在协同训练框架下结合SVM分类器构建了协同半监督模型,该模型可以同时利用有标记和无标记样本对极化SAR图像进行分类,从而在小样本时可以获得更好的分类精度;最后,为进一步改善分类结果,在协同训练分类完成后,该方法又利用Wishart分类器对分类结果进行修正。理论分析与实验表明,该算法在只有少量标记样本的情况下优于传统算法。