基于目标相干性表征差异的多波段SAR相干变化检测方法

相干变化检测(Coherent Change Detection, CCD)利用变化前后SAR图像间的相位相干性检测场景中发生的微小变化。传统的CCD方法由于对目标探测尺度单一,难以区分场景中目标变化区域与低相干干扰区域。多波段SAR对目标进行多尺度探测,依据电磁波对目标的穿透特性、目标的结构特性以及目标发生的变化尺度形成不同的相干性表征。该文据此提出一种多波段CCD方法。该方法先分别获取各个波段的相干变化差异图,然后依据目标的多波段相干性表征使用改进的期望最大化(Expectation-Maximization, EM)算法对场景分类,接下来根据少量监督样本确定目标变化类别,最后用Dempster-Shafer (DS)证据理论处理,获取多波段融合相干变化差异图。该结果可有效排除各个单波段存在的低相干干扰,达到降低虚警概率的目的。该文采用变化前后的L波段与P波段重轨SAR数据进行方法验证,实验结果与指标参数证明了该方法的有效性与正确性。      

采用混合特征相似性的极化SAR图像降噪算法

针对极化合成孔径雷达（Polarimetric Synthetic Aperture Radar,PolSAR）图像在相干斑抑制过程中面临的结构信息和极化散射信息保持的问题,通过分析PolSAR图像的结构特性和极化特性,建立一种结构特征提取方法,并提出一种采用混合特征相似性的极化SAR图像降噪算法.该算法将图像的结构信息和极化散射信息与降噪过程相融合,能够在抑制相干斑噪声的同时,实现降噪后图像的结构信息和极化信息的有效保持.实测机载极化SAR数据的实验结果验证了算法的有效性.     

基于改善极化相似性的极化SAR目标增强新方法

针对高分辨极化SAR目标增强问题,该文提出一种基于改善极化相似性的目标增强新方法。利用Huynen分解、极化方位角提取及极化方位角归零化处理消除了扰动分量、相干斑噪声及极化取向等因素对像素点极化特性的影响,改善了极化SAR图像同类区域内部像素点的极化相似性。与已有方法相比,该方法克服了区域内部像素点极化特性不一致对高分辨极化SAR目标增强的影响,可以获得理想的目标区域整体增强效果。利用全极化SAR实测数据验证了该文方法的优良性能。     

多角度极化SAR图像中的非各向同性散射估计与消除方法研究

通过对不同角度子孔径相干累加,多角度观测SAR可以提供高分辨率影像及多角度散射特征.然而,现有的累加成像方法存在非各向同性散射中心混叠问题.混叠将造成极化特征参数估计无法反映实际的目标物理特征,从而难以支撑分类及变化检测应用.为了去除不同散射中心间的相互干扰并利用不同类型的信息,该文提出了一种多角度极化SAR图像中的非各向同性散射估计与消除方法.该方法给出了基于两类目标假设的最大似然比检验统计量,分析了相干斑影响以及非各向同性散射消除机理,证明了恒虚警判决函数的单调性.通过机载P波段极化SAR进行了360观测试验,分析了非各向同性散射消除前后极化熵的变化,验证了算法的有效性并揭示出在目标特征提取方面的应用潜力.      

基于Pinball损失函数支持向量机的极化SAR图像鲁棒分类

考虑到极化合成孔径雷达(PolSAR)图像标注信息量低以及相干斑噪声难以消除的问题,该文从鲁棒统计学习的角度提出了一种基于Pin-SVM的极化SAR图像鲁棒分类方法,根据极化SAR图像的散射特性和地物的纹理特性,通过求解两类样本之间的最大分位数距离来确定分类超平面,在无需迭代的前提下得到更加鲁棒的分类结果。相比传统的基于最大间隔的极化SAR图像分类算法,该文所提算法一方面在对极化SAR图像提取到的特征中包含的噪声具有更好的鲁棒性,另一方面对于训练样本的抽样范围不敏感,即重采样具有更好的鲁棒性。利用EMISAR的Foulum地区极化SAR数据进行了算法验证,多种情况的对比实验的结果验证了该算法的有效性。      

基于子孔径分析的极化散射机理研究

在用极化sAR数据研究极化散射机理时,利用的是大量不同方位观察角度下接收的目标散射数据,但通常极化散射特性随方位角的变化并未被考虑。文中提出利用子孔径分析方法研究极化散射机理。子孔径是在不同的方位观察角度下．对整个场景的响应。用子孔径方法分析高分辨率极化sAR图像,可以突出全孔径中雷达后向散射的瞬时变化．因此可用于各向异性特征和非平稳目标的检测。最后利用丹麦EMISAR获取的L波段极化SAR全孔径图像数据进行仿真验证,提出了一种计算相对标准偏差的方法来检测非平稳目标。     

结构保持的双边滤波极化SAR图像降噪

针对极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)图像相干斑抑制时结构保持的难题,该文提出一种PolSAR图像的双边滤波算法:结构保持的双边滤波(SPBF)。该算法通过结合边缘结构特征和地物散射特性,增强对PolSAR图像结构信息的描述,减少滤波时图像结构信息的损失,实现滤波性能的提高。该算法首先使用边缘检测模板在极化总功率图像(Span)上提取边缘方向,实现自适应选择滤波方向窗;其次,采用Freeman-Durden分解获取像素的散射机制,并根据极化数据的统计分布特性获取地物散射的聚类标记;最终在所选的方向窗中,以聚类标记图为掩膜,利用改进的双边滤波算法对PolSAR数据进行相干斑抑制。真实SAR数据的实验结果表明,该方法能够有效抑制相干斑噪声,同时提高了对图像的边缘、强点目标和极化散射特性的保持能力。     

极化干涉SAR面向城区不同处理模式的误差影响分析

极化干涉合成孔径雷达(PolInSAR)在城区等复杂场景下的应用受到了越来越多的关注。面向城区的极化干涉SAR处理主要包括基于极化最优相干的干涉测高、基于极化分解的干涉测高、联立极化干涉观测方程直接求解不同散射机制高度这3种模式。现有研究对各类误差在极化干涉SAR不同处理模式下的综合影响分析尚很欠缺。该文在构建极化干涉SAR误差模型的基础上,提出了联立极化观测方程下散射机制的求解方法,推导了极化失真和干涉误差在极化干涉SAR不同处理模式下的综合影响模型,并通过仿真验证了模型的正确性,同时给出了3种处理模式补偿误差后的高度反演结果,补偿误差后通过极化最优相干得到建筑区域高度的均方根误差(RMSE)为2.77 m。在此基础上,通过仿真给出了极化干涉SAR不同处理模型下的误差影响曲线,比较了不同处理模型受误差影响的程度,并给出了合理解释,研究结果为极化干涉SAR系统设计、处理方法选择及数据应用提供了参考。      

极化SAR相干斑抑制极化白化滤波器

相干斑抑制是合成孔径雷达(SAR)图像和极化SAR图像后处理的首要问题。该文针对最优的极化相干斑抑制滤波器极化白化滤波器(PWF)仅输出一个强度图像的缺陷,对 PWF进行了改进,得到了各个极化通道的强度图像。     

基于非平稳性和极化相干系数比的PolSAR图像建筑物检测

为了提高全极化合成孔径雷达(PolSAR)图像中城区建筑物的检测精度,该文提出一种基于人造目标散射非平稳性和极化相干系数比的建筑物检测新方法。该方法首先对PolSAR图像进行滤波和方位向时频分解,得到多个子孔径图像,然后结合方位向非平稳性检测和极化相干系数比来判断某个像素是否为建筑物。该文通过引入一种新的极化相干系数比从而使获取的建筑物检测结果优于传统非平稳性检测方法,能够有效去除具有布拉格散射的自然地物虚警从而提高检测精度。星载和机载PolSAR数据实验结果验证了该方法的有效性。     

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.     

Symmetrization of cross-polarized responses in polarimetric radarimages using reciprocity

A new method for symmetrizing polarimetric scattering matrices is applied to the polarimetric synthetic aperture radar (SAR) images acquired over Mount Shasta. This method symmetrizes the cross-polarized responses in the polarimetric images using a 2×2 matrix derived from the image itself and based on the reciprocal property of natural distributed targets. The covariance parameters of the in-scene trihedral reflectors are presented to demonstrate the effectiveness of this method. The results are also compared with those obtained by the symmetrization technique employed by POLCAL, before and after crosstalk removal. Before crosstalk is removed from the images, there were no significant differences between the results obtained by the POLCAL method and the new method for the covariance parameters of trihedral reflectors. After crosstalk removal, using distributed targets with reflection symmetry, the new symmetrization method outperformed the POLCAL method     

一种极化SAR图像T型港口识别方法

针对T型港口特有的几何结构特征, 提出了一种极化合成孔径雷达(Synthetic Aperture Radar, SAR)图像T型港口识别方法.该方法利用基于区域统计特性的极化SAR数据水平集分割方法实现精确的海岸线提取.在此基础上通过曲线分裂归并算法提取岸线特征点, 并利用T型港口始末特征点的近距离特性实现感兴趣区域提取.然后采用基于链码的直线判断方法提取港口轮廓线段, 并通过判断轮廓平行直线和垂线特征实现T型港口的识别.使用伯克利地区TerraSAR极化SAR数据进行实验验证, 结果表明了提出算法的有效性, 能在大场景范围内正确识别出沿岸T型港口.     

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.     

Non-Gaussian signal statistics in ocean SAR imagery

The authors have studied the significance of non-Gaussian signal statistics in some synthetic aperture radar (SAR) images of the ocean surface. The study consisted of calculating the amplitude histogram of the returned echoes from the images and comparing these with the Rayleigh- and Kν-distributions, corresponding to the Gaussian and non-Gaussian statistics, respectively. The images used were some C-band SAR data from the Canadian airborne SAR collected during the NORCSEX'88 campaign and some ERS-1 data collected during the NORCSEX'91 campaign. The analysis of the NORCSEX'88 data included studies of the dependency of the signal statistics on incidence angle and meteorological and imaging conditions. It was found, specifically at small incidence angles, that there was a significant deviation from Gaussian statistics. It was also found that when the wind was blowing against the waves, the deviation from Gaussian statistics was more pronounced than when the wind was blowing in the same direction as the waves were propagating. The study also showed a correlation between the signal statistics and the width of the SAR image spectra. At low incidence angles, this agrees with the interpretation that non-Gaussian statistics may be related to strong widebanded scattering events. However, since non-Gaussian statistics also were observed at incidence angles as high as 50°, it is evident that the modulation of the scattering cross section by the long waves is also an important factor. In addition, the analysis of the ERS-1 data showed that to account for the width of the SAR image spectra, an azimuth smearing term, due to short scene coherence time, had to be included. This was in the present work done by modeling the short-coherence-time-smearing as a Gaussian low-pass filter. By this procedure, the authors were able to obtain realistic estimates for the average scene coherence time of the SAR scenes     

The effect of topography on radar scattering from vegetated areas

The ways in which radar scattering from vegetated areas is affected by the topography of the surface underneath the vegetation are discussed. It is shown, using a discrete scatterer model, that the dominant scattering mechanism may change drastically when the ground surface is tilted relative to the horizontal. For a horizontal ground surface, for example, the total scattering may be dominated by scattering off the tree trunks, followed by a reflection off the ground surface. For a relatively small tilt in the ground surface, the ground-trunk interaction term may be replaced by scattering from the branches alone as the dominant scattering mechanism. The effect of the topography is more pronounced for scattering by longer wavelengths, and the implications on algorithms designed to infer forest woody biomass and soil and vegetation moisture using polarimetric SAR data are discussed. The effect of the topography on the scattering behavior from forested areas is illustrated with images acquired by the NASA/JPL three-frequency polarimetric SAR over the Black Forest in Germany     

Detecting scene changes using synthetic aperture Radar interferometry

In repeat-pass interferometric synthetic aperture radar (SAR), man-made scene disturbances are commonly detected by identifying changes in the mean backscatter power of the scene or by identifying regions of low coherence. Change statistics such as the sample mean backscatter-power ratio and the sample coherence, however, are susceptible to high false-alarm rates unless the change in the mean backscatter power is large or there is sufficient contrast in scene coherence between the changed and unchanged regions of the image pair. Furthermore, as the sample mean backscatter-power ratio and sample coherence measure different properties of a SAR image pair, both change statistics need to be considered to properly characterize scene changes. In this paper, models describing the changed and unchanged regions of a scene are postulated, and the detection problem is expressed in a Bayesian hypothesis-testing framework. Forming the log-likelihood ratio gives a single sufficient statistic, encoding changes in both the coherence and the mean backscatter power, for discriminating between the unchanged- and changed-scene models. The theoretical detection performance of the change statistic is derived and shows a significant improvement over both the sample mean backscatter-power ratio and sample coherence change statistics. Finally, the superior detection performance of the log-likelihood change statistic is demonstrated using experimental data collected using the Defence Science and Technology Organisation's Ingara X-band airborne SAR.