基于支持向量机的极化SAR图像分类

与传统最大似然（ML）分类器相比，支持向量机（SVM）在小训练样本时仍具有良好的分类性能，目前已广泛应用于多个领域。该文在极化SAR特征提取的基础上，将SVM应用于极化SAR图像分类，分析了分类器参数对分类性能的影响。利用NASA／JPL实验室AIRSAR系统的L波段旧金山全极化SAR数据比较了SVM和ML的分类性能，并进一步给出了基于SVM的国内某地区双极化SAR图像分类结果。     

一种双频极化SAR图像分类方法

本文提出了一种利用两种不同频率下的极化SAR图像进行地物分类的新方法,该方法是基于目标的散射特性随频率变化而改变的趋势和程度实现的。基于不同频率下所提取的特征量,定义了特征变化量和特征变化平面。本论文选择了极化熵变化量 和极化度变化量 作为特征,通过将 平面分割为9个区域,进而将目标分为9个类。这种方法反映了目标散射特性随频率的变化关系,物理意义直观,实现方法简单易行。将这种分类方法与Wishart分类器相结合,就可以实现对极化SAR图像的无监督迭代分类。实测的SIR-C数据的分类结果表明,该方法是一种有效的极化SAR图像分类方法。      

基于CNN和RFC的极化SAR图像分类

卷积神经网络(Convolution Neural Network,CNN)在极化合成孔径雷达(Synthetic Aperture Rader,SAR)图像分类的应用中在类别非边界区域取得了好的分类结果,在类别边界区域没有取得好的分类结果,随机森林分类器(Random Forest Classifier,RFC)在极...     

SAR图像的极化干涉非监督Wishart分类方法和实验研究

该文在合成孔径雷达图像的极化非监督Wishart分类的基础上,给出了一种利用极化干涉信息对合成孔径雷达图像进行非监督分类的方法。该方法主要利用一(66)的极化干涉相关矩阵,从而可以同时考虑单幅图像的全极化信息以及两幅像对之间的互相关信息。该文详细阐述了该方法的具体实现,并利用NASA/JPL的SIR-C/X-SAR系统在中国天山地区的L波段实测数据进行了实验研究。给出了利用该方法对实验数据进行分类的结果,并与极化非监督Wishart分类的结果进行了比较。结果表明,该方法能够很好地分辨不同类型的地物,保持地物的细节,并且比极化非监督Wishart分类结果有很大改善。     

利用0-1矩阵分解集成的极化SAR图像分类

全极化合成孔径雷达(PolSAR)图像蕴含更丰富的散射信息,具有更多的可用特征。如何使用这些特征是极化SAR图像分类中非常重要的一步,但是目前尚未对此提出非常明确的准则。为了能够有效地解决上述问题,该文提出一种基于特征加权集成的极化SAR图像分类算法。该算法采用0-1矩阵分解集成方法对包括不同特征的数据集进行学习获得相应加权系数,并通过对每个特征集获得的预测结果进行加权集成来提高极化SAR图像分类性能。首先,输入极化SAR数据,获得极化特征作为原始特征集,并对其进行随机抽取获得不同的特征子集;然后,使用0-1矩阵集成算法得到每个特征值相对应的加权系数;最后,通过对各个特征子集的预测结果进行集成得到最终极化SAR图像分类结果。实测L波段和C波段极化数据的实验结果表明,该算法可以有效地提高极化SAR图像分类的准确度。     

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。      

极化SAR图像分类综述

对极化SAR(合成孔径雷达)图像分类进行综述.首先建立简略的极化SAB图像分类技术发展流程,并对国内外研究机构在该领域的主要研究成果进行归纳;然后对用于极化SAR图像分类的众多特征进行分类整理,指出其物理意义及在图像分类中的优缺点,并从有监督和无监督的角度综述了现有的极化SAR图像分类方法;最后对极化SAR图像分类存在的问题进行归纳,并指出将来的研究方向.     

基于H-α和改进C-均值的全极化SAR图像非监督分类

该文提出一种基于H-α和改进C-均值的全极化SAR图像非监督分类方法.该方法先按H-α对全极化SAR图像进行基于散射机理的分类,再将分类结果作为改进C-均值算法的初始类别划分,从而实现地物分类.迭代次数确定是C-均值动态聚类算法的关键,文中利用图像熵给出了一种新的迭代终止准则.与H-α方法相比,该文方法能在保留分类结果物理散射机理的同时,实现有效的地物分类.NASA/JPL实验室AIRSAR系统获取的L波段旧金山全极化SAR数据的实验结果验证了该文方法的有效性.     

AdaBoost算法实现全极化SAR图像分类

文中提出了一种基于AdaBoost算法的全极化SAR(Synthetic Aperture Radar)图像分类方法.该方法将AdaBoost算法与HH、HV和VV三个极化通道数据结合起来,对全极化SAR图像进行分类,充分利用了极化信息和AdaBoost算法的快速收敛性.将该方法的仿真结果与H/α分类方法仿真结果进行比较,发现该方法分类模糊程度较低,在细节上分类更为准确,且在相同的情况下,该算法速度更快.     

一种结合散射相似性和Wishart的极化SAR图像分类方法

针对基于H-a 平面的目标分类方法运算量偏大的缺点,本文提出了一种结合散射相似性和Wishart的极化合成孔径雷达图像分类方法,提升了数据处理效率。该方法使用参数替代,将目标相干矩阵进行归一化,得到和 H-a 相似的分类效果,且避免计算特征值和特征向量,从而大幅提高了分类效率;通过结合Wishart迭代分类器进行分类来提升分类精度。最后通过对Radarsat-2卫星获取的黄河冰凌区域进行地物分类实验验证了本文方法的有效性,且该方法具有更好的运算效率和地物分类效果。      

Polarimetric SAR image segmentation with B-splines and a new statistical model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the G_P^H{\mathcal{G}_P^H} distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric G_P^H{\mathcal{G}_P^H} model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented.     

基于变分法的图像分割的数值算法

解释一种图像分割问题的数学模型，并讨论图像分割问题与Mumford-Shah泛函的关系，对此问题首先就一维的情形提出一种可行的解法并推广到二维。若干试验结果验证了算法的有效性。     

A convex variational method for super resolution of SAR image with speckle noise

Super resolution (SR) is an attractive issue in image processing. In the synthetic aperture radar (SAR) image, speckle noise is a crucial problem that is multiplicative. Therefore, numerous custom SR methods considering additive Gaussian noise cannot respond to this image degradation model. The main contribution of this paper is to propose a novel variational convex optimization model for the single SAR image SR reconstruction with speckle noise that is one of the first works in this field. Employing maximum a posteriori (MAP) estimator and proposing an effective regularization based on combination of sparse representation, total variation (TV) and a novel feature space based soft projection tool to use merits of them is the main idea. To solve the proposed model, the split Bregman algorithm is employed efficiently. Experimental results for the multiple synthetic and realistic SAR images show the effectiveness of proposed method in terms of both fidelity and visual perception.     

Polarimetric SAR image signatures of the ocean and Gulf Streamfeatures

Polarimetric signatures and related polarimetric properties of microwave ocean backscatter are analyzed for both the ambient ocean and for ocean features such as those associated with the Gulf Stream. Interpretation of the polarimetric signatures for the ocean surface is accomplished using a tilted-Bragg theoretical model. This model is used to calculate the EM fields, to second order, which is necessary to compute the full Stokes matrix and, ultimately, the polarimetric signature. The polarimetric studies lead to a technique for potentially improving the visibility of all azimuthally traveling waves in real-aperture radar (RAR) images and very long waves in synthetic-aperture radar (SAR) images. This technique utilizes linear polarization signatures to maximize the instrument sensitivity to azimuthally traveling waves. Wave tilts create a modulation of the cell polarization orientation which, in turn, modulates the backscatter. Critical to the success of this technique is that the ocean polarimetric signatures be sharply peaked (i.e., returns be highly polarized). The polarimetric contribution to the overall modulation transfer function is evaluated     

An image pixel based variational model for histogram equalization

In this paper, we develop an image pixel based histogram equalization model for image contrast enhancement. The approach is to propose a variational model containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform. This idea is different from existing histogram equalization algorithms where a histogram based on the input image is constructed, a mapping is determined to output a uniform histogram and then the pixel values of the input image are adjusted based on the mapping. In the variational model, a mean brightness term is incorporated to preserve the brightness of the input image, and a geometry constraint can also be added to keep the geometry structure of the input image. Theoretically, the existence of the minimizer of the proposed model, and the convergence of the proposed algorithm are given. Experimental results are reported to demonstrate that the performance of the proposed model are competitive with the other testing histogram equalization methods for several testing images.     

SAR image filtering based on the heavy-tailed Rayleigh model.

Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal.     

基于改进自调整图谱方法的SAR图像分割

谱聚类是在给定数据集上用基于图论的方法进行分类,并已广泛地应用于SAR图像分割.自调整谱聚类（self-tuning spectral clustering,简称STSC）方法是一种可以自动确定尺度因子和分类数的方法.本文给出了一种改进的STSC方法,使用熵函数作为自动求分类数的代价函数,使得分类数的计算更加准确和有效,提高了方法的分类精度.实验表明,改进的STSC方法对自然图像、SAR图像的分割精度高于原STSC方法.     

一种新的基于ICA的SAR图像干扰分离方法

干扰后的合成孔径雷达(SAR)图像可以看作是目标信号和干扰信号的混合信号源,信号空间和干扰信号空间认为是统计独立的,因此图像域上的干扰抑制问题可以转化为一种信号分离问题.借助于独立分量分析(ICA)方法,提出高阶统计量-峭度值作为基图像分类标准,实现了干扰信号的有效抑制.理论分析和对高斯白噪声干扰的仿真实验验证了方法的有效性.     

基于区域谱聚类的极化合成孔径雷达图像分割

极化合成孔径雷达(Synthetic Aperture Radar,SAR)经常用于地物图像的分割和分类.实际中监测范围广,需要算法快速有效;地物复杂,需要算法能够处理不均匀地物.针对上述问题,提出了基于区域合并和谱聚类的极化SAR图像分割方法.先对图像进行一个区域合并步骤完成粗分割,产生许多具有相似统计特性的区域块,再对过分割的区域块进行谱聚类.多个场景下的实验表明:所提方法相对于传统针对像素点的谱聚类,运算复杂度低;相对于完全进行区域融合的方法,更能适应不均匀地物和大场景分割.     

极化SAR目标相对最优极化研究

极化SAR通过测量地物目标的散射回波得到极化散射矩阵或Stokes矩阵,利用极化合成技术得到任意发射和接收极化组合下的天线接收功率。目标相对最优极化就是选取一种收发极化状态使得研究目标和背景地物的接收功率对比度达到最大。文中首先分析了目标相对最优极化的模型,指出了它们的优缺点,然后提出了一种新的改进模型,并用交替迭代方法进行了模型的解算,最后结合荷兰Flevoland地区全极化数据作了试验,证明了其有效性和正确一陆。