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.     

Modeling SAR images with a generalization of the Rayleigh distribution

Synthetic aperture radar (SAR) imagery has found important applications due to its clear advantages over optical satellite imagery one of them being able to operate in various weather conditions. However, due to the physics of the radar imaging process, SAR images contain unwanted artifacts in the form of a granular look which is called speckle. The assumptions of the classical SAR image generation model lead to a Rayleigh distribution model for the histogram of the SAR image. However, some experimental data such as images of urban areas show impulsive characteristics that correspond to underlying heavy-tailed distributions, which are clearly non-Rayleigh. Some alternative distributions have been suggested such as the Weibull, log-normal, and the k-distribution which had success in varying degrees depending on the application. Recently, an alternative model namely the alpha-stable distribution has been suggested for modeling radar clutter. In this paper, we show that the amplitude distribution of the complex wave, the real and the imaginery components of which are assumed to be distributed by the alpha-stable distribution, is a generalization of the Rayleigh distribution. We demonstrate that the amplitude distribution is a mixture of Rayleighs as is the k-distribution in accordance with earlier work on modeling SAR images which showed that almost all successful SAR image models could be expressed as mixtures of Rayleighs. We also present parameter estimation techniques based on negative order moments for the new model. Finally, we test the performance of the model on urban images and compare with other models such as Rayleigh, Weibull, and the k-distribution.     

Novel Bayesian multiscale method for speckle removal in medical ultrasound images

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, we compare our technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantify the achieved performance improvement.     

基于斑点方差估计的非下采样Contourlet域SAR图像去噪

 合成孔径雷达(SAR)图像固有的相干斑噪声严重影响图像质量,使得SAR图像的自动解译十分困难.本文联合SAR图像的统计特性和非下采样Contourlet对SAR图像细节信息的良好刻画能力,提出一种新的非下采样Contourlet域SAR图像去噪算法,通过估计到的各个高频方向子带的斑点噪声方差和变换系数模值的局部均值,对非下采样Contourlet变换系数进行判定,保留信号系数,抑制斑点噪声系数,实现SAR图像去噪.仿真实验结果表明,本文方法在斑点抑制的同时可以有效保持细节信息.     

基于差异度的极化SAR相干斑抑制效果评估

相干斑抑制是极化SAR图像解译和信息提取的基础。针对采用极化特征图对极化SAR图像相干斑抑制算法极化信息保持能力定性评估的不足,本文提出一种基于差异度的相干斑抑制效果评估方法。该方法通过比较相干斑抑制前后目标区域的差异度,实现相干斑抑制算法对目标极化信息保持能力的定量评估。在评估过程中采用分别先对目标区域内每个像素做差异度后求平均的方法,代替传统的先将目标区域内像素K矩阵求平均后求极化特征图的方法,避免了先对各个像素做平均带来的二次估计。对通过极化特征图判断性能接近的不同相干斑抑制算法,得到更为精确的评估结果。最后选取三类典型相干斑抑制算法,分别对仿真数据和实测ESAR数据中的面目标和点目标进行评估,验证了方法的有效性。      

SAR图像组合降斑算法

SAR图像存在较强的相干斑点噪声,严重的影响了地物信息的提取与SAR图像的应用效果,作者在研究分析斑点噪声产生机理以及统计特性的基础上,提出了一种组合降斑重构算法,该算法与降斑重构性能优异的模拟退火算法降斑重构性能相当,但极大的减少了计算代价;与许多其它SAR降斑算法相比,组合降斑重构算法具有很好的降斑以及场景结构保持性能,并有较好的实用性.     

二进小波增强与边缘局部信息FCM的SAR图像变化检测

合成孔径雷达图像中固有的相干斑噪声往往导致变化检测结果中存在大量虚警与漏警。针对这一问题,本文提出一种利用二进小波增强与边缘局部信息模糊C均值的变化检测方法。首先利用二进小波对对数比差异图进行自适应增强,平抑噪声的同时均衡灰度分布;然后,利用指数加权均值比算子对差异图进行边缘信息提取,修正局部信息模糊C均值算法中邻域窗内像素点权值,使邻域窗滑动至变化区域的边缘部分时能够对噪声切向平抑,保留细节信息。最后对差异图进行分割,得到变化检测结果二值图。仿真与实测数据实验结果表明,本文方法能够有效抑制相干斑噪声,同时对变化区域的细节保持效果较好。      

Information-theoretic heterogeneity measurement for SAR imagery

A heterogeneity feature, calculable from synthetic aperture radar (SAR) images on a per-pixel basis, but relying on global image statistics, is defined and discussed. Starting from the multiplicative speckle and texture models relating the amount of texture and speckle to the local mean and variance at every pixel, such a feature is rigorously derived from Shannon's information theory as the conditional information of local standard deviation to local mean. Thanks to robust statistical estimation, it is very little sensitive to the noise affecting SAR data, and thus capable of capturing subtle variations of texture whenever they are embedded in a heavy speckle. Experimental results carried out on two SAR images with different degrees of noisiness demonstrate that the proposed feature is likely to be useful for a variety of automated segmentation and classification tasks.     

结合ICA相干斑抑制的全极化SAR图像分类

本文提出了一种结合相干斑抑制的全极化 SAR(Synthetic Aperture Radar)图像分类新方法.该方法先对图像数据做Pauli分解,获得三个极化组合通道,并分别用三种颜色表示这三个极化组合;再用独立分量分析稀疏编码(ICA-SCS)算法对各颜色通道进行相干斑抑制,最后把三个颜色通道混合,实现了对图像信息的分类.该方法很好的保留了极化通道间的相对相位信息,同时,相干斑抑制后的数据直接用于图像分类,不需要再做任何极化通道组合.对真实SAR图像的分类结果表明,该方法对分类效果和精度有明显改善.     

Speckle Suppression in SAR Images Using the 2-D GARCH Model

A novel Bayesian-based speckle suppression method for Synthetic Aperture Radar ( SAR) images is presented that preserves the structural features and textural information of the scene. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the wavelet coefficients of SAR images have significantly non-Gaussian statistics that are best described by the 2-D GARCH model. By using the 2-D GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution and the dependencies between the coefficients. Furthermore, we use a maximum a posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on synthetic and actual SAR images and we verify the performance improvement in utilizing the new strategy.     

面向舰船目标检测的单通道复值SAR图像统计建模方法研究

合成孔径雷达(SAR)成像模式丰富、覆盖范围广、分辨率高，可以长期、动态、宏观地对海洋进行监测。在完全发展的相干斑假设条件下，传统单通道SAR图像舰船目标检测方法主要研究幅度信息。然而，其部分假设条件在高分辨率情形下并非严格成立，因此无法有效利用单通道SAR图像的相位或复值信息。该文面向舰船目标检测应用，将单通道复值SAR图像统计建模方法划分为幅度、相位和复值统计建模3个部分，首先简要综述了单通道SAR图像幅度统计建模方法，然后详细阐述了单通道SAR图像相位和复值统计建模方法，并重点介绍了其建模过程和参数估计方法。在此基础上，该文给出了作者研究小组在基于复值统计信息的单通道SAR图像舰船目标检测方面的部分最新研究结果，并分析展望了下一步研究方向。      

基于PSO优化空间约束聚类的SAR图像分割

相干斑噪声的存在使得合成孔径雷达(SAR)图像的分割问题变得非常复杂.本文提出一种具有鲁棒抗噪特性的SAR图像分割方法,该算法通过将多尺度条件下的边缘信息以及相邻像素的相对位置和强度信息所构成的空间信息融入模糊C-均值(FCM)聚类算法的相似性测度中,增强了分割方法的抗噪性.空间信息对FCM聚类算法的调控由粒子群优化(PSO)算法完成,优化的调控参数有助于获得良好的分割结果.该算法对初始分割不敏感,并具有鲁棒的抗噪性能.MSTAR数据的分割实验结果表明:该算法能够有效地分割SAR图像,与通过改进型FCM(IFCM)算法获得的分割结果比较,分割结果明显改善.     

利用小波分析来抑制合成孔径雷达图象的相干斑噪声

本文利用小波变换来对合成孔径雷达图象做多分辨率分析,说明了相干斑噪声对图象的不同频率成份的影响程度是不同的,同时也说明了相干斑噪声与图象在小波变换下具有截然不同的奇异性.因此,本文提出了用小波分析来抑制相干斑噪声。实验证明了:小波分析除了能够对相干斑具有很好的抑制效果外,还保留了尽可能多的原图象的目标特征。利用小波分析来抑制相干斑噪声是一个十分吸引人的新方法。     

基于复小波方向信息的SAR图像斑点噪声抑制

针对一般小波去噪方法在去除合成孔径雷达(Synthetic Aperture Radar-SAR)图像斑点噪声时不能有效保持图像边缘信息的问题,提出结合双密度双树复小波变换（Double-Density Dual Tree Complex Wavelet Transform –DD_DTCWT）方向信息进行边缘检测的SAR图像噪声抑制算法。本文对边缘检测指标进行改进,利用DD_DTCWT方向复小波系数的相对方差作为边缘检测指标,通过相对方差分布密度函数获取阈值处理的自适应门限,由此实现SAR图像的自适应滤波。实验结果表明,本文提出的边缘检测和主方向高频复系数提升方法可以有效保持并增强图像的边缘信息。与SRAD算法和基于DD_DTCWT的双变量收缩函数（Bivariate Shrinkage Function--BSF）算法相比较,本文算法具有更好的边缘保持能力。      

一种新的极化滤波综合性能评估方法

极化合成孔径雷达(SAR)可以获得目标散射的相位信息,这种更加丰富的信息为极化SAR图像相干斑噪声抑制提供了更好的解决方法。通过对几种典型的极化滤波技术的算法实现,基于采样信号波形图、极化特征图和相对标准差,提出了一种新的极化滤波综合性能评估方法。对实测极化SAR数据进行实验验证,结果表明局部统计滤波在图像分辨率的保持、极化信息的保留和相干斑噪声的抑制3个方面都有很好的效果。     

Statistical properties of logarithmically transformed speckle

In synthetic aperture radar (SAR) image processing and analysis, the logarithmic transform is often employed to convert the multiplicative speckle model to an additive noise model. However, this nonlinear operation totally changes the statistics of SAR images. In this communication, we first review the statistical properties of speckle noise in both the intensity and the amplitude formats. Then, we derive the probability density functions, the mean values, and the variances to characterize the log-transformed speckle. Finally we discuss the problems introduced by the logarithmic transform on statistical analysis of SAR images. The statistical models developed in this communication will facilitate subsequent SAR image processing tasks based on the additive noise model     

基于2D-PWVD的SAR图像相干斑噪声抑制

合成孔径雷达(SAR)图像相干斑噪声是影响对SAR图像正确解译的主要因素之一,在二维魏格纳分布的框架内,提出了一种基于二维伪魏格纳威尔分布变换的SAR图像噪声抑制的方法。首先,根据魏格纳威尔分布的理论,阐述了图像的二维伪魏格纳威分布表示,以及分解后的多频段图谱的特性;然后,针对该特性,提出对应的计算处理方法,并将分解图像相加,形成SAR图像噪声抑制后图像;最后,利用MiniSAR实测SAR图像数据进行验证试验,并将结果与均值滤波、LEE滤波和小波软阈值滤波SAR图像噪声抑制算法进行对比分析,结果显示,文中所提算法是有效可行的。     

基于数据融合的SAR图像斑点噪声抑制

合成孔径雷达图像中的斑点噪声极大地降低了图像的可读性，不利于图像解译和信息提取。根据均值滤波和小波软阈值去噪算法的优点，提出了一种基于数据融合的合成孔径雷达图像相干斑抑制算法。该方法首先对含噪图像进行均值滤波和小波软阈值去噪，再对滤波后的两幅图像在小波域进行数据融合。实验结果表明，这种方法抑制SAR图像斑点噪声的效果较好。     

一种基于斑点抑制的SAR图像舰船航迹检测算法

该文分析了合成孔径雷达(SAR)图像中舰船航迹的特性和斑点噪声模型及其局部统计特性。在此基础上,提出了一种先基于小波变换进行斑点噪声抑制,再基于Radon变换进行航迹检测的方法。对数据处理的结果表明,该方法较直接对SAR图像应用Radon变换能更有效、准确地检测到SAR图像中的舰船航迹。     

基于无监督分类的多视极化SAR相干斑滤波

相干斑噪声是引起SAR图像降质的主要原因之一。多视极化白化滤波器(MPWF)是一种专门应用于多视极化SAR图像降噪的有效技术。其中,滤波器参数估计的精确度直接决定了其滤波性能的好坏。对此,该文提出了一种新的基于无监督分类的自适应窗算法。该算法以分类图像作为对象;在滑动矩形窗内以中心像素作为参照物,自动搜索与其同类的像素并用于MPWF参数估计。实验结果表明,与其他几种典型的算法相比,该法不仅有效地抑制了相干斑,而且对图像的纹理信息具有很好的保持能力。