Adaptive target detection in foliage-penetrating SAR images usingalpha-stable models

Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (UWB SAR) images is an important and challenging problem. Given the different nature of target returns in foliage and nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, conventional detection algorithms fail to yield robust target detection results. A new target detection algorithm is proposed that (1) incorporates symmetric alpha-stable (SalphaS) distributions for accurate clutter modeling, (2) constructs a two-dimensional (2-D) site model for deriving local context, and (3) exploits the site model for region-adaptive target detection. Theoretical and empirical evidence is given to support the use of the SalphaS model for image segmentation and constant false alarm rate (CFAR) detection. Results of our algorithm on real FOPEN images collected by the Army Research Laboratory are provided.     

SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.     

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.     

SAR图像的Gamma混合分布建模方法

随机信号的混合概率模型比单一概率模型具有更多的灵活性,更适合复杂的分布建模。当前主要的混合概率模型有高斯混合模型、α分布混合模型和Gamma混合模型等。但高斯混合模型更适合随机变量对称分布的分布建模,而α混合模型参数多、算法复杂。SAR图像的像素值为非负值,且多为斜峰分布,更适合用Gamma混合模型建模。仿真分析及数据测试都表明,本文提出的gamma混合分布建模方法对SAR图像的像素统计分布具有更高的运算效率。      

应用Alpha稳定分布对雷达杂波的辨识

该文引入正向Alpha稳定分布辨识常见的瑞利、韦布尔、对数正态和K分布雷达杂波,以该分布模型参数表征4类雷达杂波的拖尾分布。提出了以Alpha稳定分布模型参数作为杂波分类特征的雷达杂波辨识的新方法,实现了对常见4种杂波分布的辨识。仿真结果表明,同经典的KS假设检验方法相比,该方法辨识精度高、运算量小。     

极不均匀合成孔径雷达杂波建模及恒虚警检测

基于合成孔径雷达(SAR)城区极不均匀杂波实际数据和Cramer-Von Mises距离,本文运用Rayleigh分布、Weibull分布、K分布和G分布对杂波进行了分布拟合检验.结果表明Rayleigh分布和Weibull分布难以拟合极不均匀场景回波,K分布只能部分拟合,而G分布的拟合程度最好.进而,针对G分布下的目标恒虚警检测问题,本文给出了不同参考单元条件下达到给定检测性能时信噪比随形状参数的变化曲线,分析了恒虚警(CFAR)性能损失随分布参数变化的原因,得到了确定恒虚警门限的有效方法.     

A new statistical model for Markovian classification of urban areas in high-resolution SAR images

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.     

几何模型约束的SAR图像建筑物提取

利用高分辨率SAR图像进行建筑物提取的常规方法是首先利用二次散射特征线确定建筑物边界, 然后利用叠掩、阴影等散射特征来提取建筑物高度.当建筑物目标走向与星载SAR方位向夹角较大时, 其二次散射特征不明显, 常规重建方法不能取得理想结果.针对这类建筑物目标, 在分析SAR图像上的散射特征为平行四边形条带的基础上, 提出一种基于几何模型约束的建筑物自动提取与三维重建方法.将该方法应用于TerraSAR-X聚束模式图像, 并对提取结果进行了分析和评价, 表明该方法能够有效提取建筑物目标及其三维信息.     

Evaluation of terrain models for the geocoding and terraincorrection, of synthetic aperture radar (SAR) images

The variability of the resolutions and the presence of artifacts cause inaccurate correction of the terrain-induced geometric distortions in synthetic-aperture radar (SAR) images. To quantify the effects of these inaccuracies on SAR terrain correction, corrections of a Seasat SAR image were performed using a 1° US Geological Survey (USGS) terrain model, a 7.5-min USGS terrain model, and a terrain model derived from stereoimagery acquired from SPOT. Geometric verifications of the corrected imagery showed that the resolution of the 1° terrain model is not adequate to resolve many features in the Seasat image. Geometric verifications of images corrected with the two higher resolution terrain models showed localized errors as large as 52 m for mountain peaks. However, comparison of data corrected with those models shows that both produce results that differ by less than the resolution of either of them. Periodic artifacts observed in the terrain models translated to ground range differences of 18 m, which are well below the resolution of the SAR imagery     

SAR图像基于Rayleigh分布假设的最小误差阂值化分割

针对合成孔径雷达(SAR)图像的特点,本文提出基于灰度直方图的混合偏移Rayleigh分布假设下的最小误差阈值化分割算法,并与现有的基于Gauss和Poisson分布假设下的最小误差分割算法以及经典的Otsu算法作了比较。实验和Kolmogorov-Smirnov检验结果表明对SAR图像而言,基于Rayleigh假设的算法可以取得更好的分割效果。     

一种新的高频雷达海杂波的分形模型

利用分形理论建立的海杂波模型具有巨大的潜力,在高频雷达海杂波多重分形特性的基础上建立了高频雷达海杂波的时域分形模型。通过对韦布尔分布、对数正态分布、瑞利分布和K分布4种最常用的海杂波的概率密度函数的比较分析并利用修正的Kolmogorov-Smirnov(K-S)统计检验,得出了高频雷达海杂波的时域模型具有先验的统计特性的结论,证明了新的海杂波时域分形模型的合理性,这对于高频雷达海杂波建模与仿真及其背景下目标检测研究具有现实意义。     

Lognormal Random-Field Models and Their Applications to Radar Image Synthesis

Lognormal random fields with multiplicative spatial interaction are proposed for modeling radar image intensity. Two particular classes of two-dimensional (2-D) lognormal random fields are introduced: multiplicative autoregressive (MAR), and multiplicative Mnrkov random fields (MMRF). The MAR and MMRF models are formulated as invertible point-transformations of Gaussian autoregressive and Gaussian Markov random fields (GMRF) and therefore possess many desirable properties. Least squares and maximum likelihood estimates for random-field parameters are presented, a decision rule is developed for selecting model order and transformations to normality as well, and techniques for synthesizing 2-D lognormal random fields are provided. Several Seasat synthetic aperture radar (SAR) images were tested using the decision rule developed in this paper and using the Kolmogorov-Smirnov (K-S) goodness-of-fit test. With both tests they were found to possess a good fit to lognormal statistics. MAR and MMRF models were fit to Seasat SAR images, and then the models were used to generate synthetic images that closely resemble the original SAR images both visually and in their variograms. This demonstrates the generality and appropriateness of the MAR and MMRF models for radar imagery.     

Removal of terrain effects from SAR satellite imagery of Arctictundra

Synthetic aperture radar (SAR) images of the Earth's terrestrial surface contain geometric and radiometric image effects which are caused by varying terrain elevation and slope. The radiometric effects tend to mask signal variations caused by other physical variables such as soil moisture and surface vegetation type, which are known to influence SAR backscatter signals. As a result, raw SAR images are of limited use in classifying surface vegetation type or quantifying the spatial distribution of soil moisture in regions of terrain relief, The authors present a technique for removing radiometric terrain effects from SAR images. Image correction was carried out in two steps. First, an existing modeling package was used in combination with digital elevation data in order to map the raw image pixels onto a geodetic coordinate system, thereby removing the geometric portion of the image distortion. Radiometric effects were then removed with the aid of a backscatter model which treats the reflected radiation as a combination of diffuse-Lambertian and specular components. Parameters in the backscatter model were determined by comparing two C-band SAR images of a test area in a region of Arctic tundra which were taken from ascending and descending orbit tracks of the ERS-1 satellite. The ascending and descending images displayed reductions in pixel value variance of 30% and 13%, respectively, after processing. Direct comparison of the two test area images reveals a dramatic improvement in image similarity after processing     

Automated tracking of ice floes: a stochastic approach

A method for tracking ice floes in sequential satellite imagery is presented. The approach, based on probability distributions, directly incorporates the spatial information about feature locations into the estimation procedure. Given an image taken at time t₀, a probability model is used to determine how features in the image will appear at time t₁, and the probability distribution is used to identify features common to both images. The use of a probability model provides a means to measure the goodness of fit of the resulting matches. The features used are the outlines of sea ice floes observed in SAR (synthetic aperture radar) images, although the method can be applied to any set of features. The floe outlines are found using an erosion-propagation algorithm which combines erosion from mathematical morphology with local propagation of information about floe edges     

Adaptive enhancement of compressed SAR images

Synthetic aperture radar (SAR) imagery is an important global all-weather surveillance and mapping satellite imagery system. As space-borne systems have a limited storage capacity, it is imperative to heavily compress SAR images, possible with lossy compression schemes. As a result, SAR images need to be enhanced in earth stations. The work reported in this paper aims to address the issue of compression artefact removal of SAR images in an adaptive manner. The SAR images, compressed using the JPEG utility at significantly low bit rates, are enhanced by adaptively removing coding artefacts and speckle noise. As edges carry significant information in satellite imagery, a significant edge image is used for edge enhancement with selective removal of noisy edges. Further, an image sharpness metric is proposed in this work to serve as an objective no-reference metric for measuring the sharpness of SAR images.     

海杂波的K-分布建模与仿真

海杂波的建模与仿真是雷达环境模拟的重要部分。仿真得到的海杂波数据良好与否是雷达设计及雷达信号处理的关键。通常采用复高斯过程的方法来对雷达杂波建模,在幅度上等价于瑞利分布。然而,随着现代雷达分辨率的提高及/或在低入射余角的情况下,测量到的海杂波幅度的概率密度函数(PDF)呈现出与瑞利分布很大的偏差,而与K-分布模型比较吻合。该文利用K-分布对海杂波进行建模与仿真,从统计学的观点出发理解海杂波的特性,以期能提高海杂波的检测性能。     

Restoration of speckle noise corrupted SAR images using regularization by denoising

Speckle noise removal is a well-established problem in synthetic aperture radar (SAR) image processing. Among different methods focused on the reconstruction of SAR images, variational models have achieved state-of-the-art performance. In this paper, a Rayleigh based speckle reduction algorithm is developed using the variational framework. The forward model is combined with recently proposed regularization by denoising (RED) prior. However, RED has been proposed in literature for the additive noise model. Multiplicative noise in SAR images prevents the direct application of RED to variational models. Hence, logarithm transformation is applied to change the multiplicative noise model to additive model, and the forward model from Rayleigh to Fisher–Tippett distribution. The resulting optimization problem is solved using the alternating direction method of multipliers. Further, the proof of the convergence analysis is carried out for the above framework. Simulations convey that the proposed method has better despeckling performance compared to that of state-of-the-art methods.     

一种海洋涡旋SAR图像仿真方法

海洋涡旋对海洋热循环起着关键作用,是海洋科学研究中的一个重要分支。合成孔径雷达(Synthetic Aperture Radar, SAR)为海洋涡旋的观测和研究提供了大量的图像数据,但是涡旋在SAR成像时会受到各种海洋环境因素的影响,难以解译涡旋SAR图像特征。仿真SAR图像可以用于研究涡旋的特征,但是目前极少有关于涡旋SAR图像仿真方法的研究。为了更好地解译SAR图像中的涡旋特征,该文提出了一种涡旋SAR图像仿真方法。首先,基于流体力学中典型的Burgers-Rott涡旋模型,建立涡旋2维表面流场;然后,利用SAR海洋成像仿真模型,仿真给定涡旋2维流场、海面风场以及雷达系统参数下的涡旋SAR图像。该文针对气旋式涡旋与反气旋式涡旋进行了仿真实验,并建立了仿真涡旋SAR图像的相似度评价标准。实验结果表明,仿真的涡旋SAR图像与真实星载涡旋SAR图像能够较好地吻合,验证了方法的有效性。      

Rayleigh-distribution based minimum error thresholding for SAR images

This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.     

基于Matlab的雷达杂波建模与仿真

针对现代雷达的几种典型的杂波环境，讨论了Rayleigh分布、Weibull分布和K分布三种杂波，给出了三种不同分布杂波的数学模型以及实现框图，并运用Matlab对三种杂波进行了仿真。