Millimeter-wave radar phenomenology of power lines and apolarimetric detection algorithm

The radar phenomenology of high-voltage power lines and cables is studied for examining the feasibility of detecting power lines along the path of a low-flying aircraft using a millimeter-wave radar system. For this purpose, polarimetric backscatter measurements of power line samples of different diameters and strand arrangements were performed over a wide range of incidence angles with very fine increments at 94 GHz. Also, similar polarimetric backscatter measurements were conducted for cylinders of the same radii and lengths as the power line samples for identifying the scattering features caused by the braiding structure of the power lines. In addition, the effects of a thin layer of water and a layer of ice over the power line surface on its polarimetric scattering behavior are studied by repeating the polarimetric backscatter measurements. Based on this phenomenological study, a polarimetric detection algorithm that makes use of the scattering features caused by the braided structure of power lines is proposed. It is shown that the proposed algorithm is capable of detecting power lines in a relatively strong clutter background with a poor signal-to-clutter ratio. The performance of the algorithm is demonstrated experimentally using a rough asphalt surface and a vegetation foliage as sample clutter backgrounds     

Fractional Brownian motion models for synthetic aperture radarimagery scene segmentation

The application of fractal random process models and their related scaling parameters as features in the analysis and segmentation of clutter in high-resolution, polarimetric synthetic aperture radar (SAR) imagery is demonstrated. Specifically, the fractal dimension of natural clutter sources, such as grass and trees, is computed and used as a texture feature for a Bayesian classifier. The SAR shadows are segmented in a separate manner using the original backscatter power as a discriminant. The proposed segmentation process yields a three-class segmentation map for the scenes considered in this study (with three clutter types: shadows, trees, and grass). The difficulty of computing texture metrics in high-speckle SAR imagery is addressed. In particular, a two-step preprocessing approach consisting of polarimetric minimum speckle filtering followed by noncoherent spatial averaging is used. The relevance of the resulting segmentation maps to constant-false-alarm-rate (CFAR) radar target detection techniques is discussed     

复杂杂波背景下海洋SAR图像中舰船目标的检测

针对卫星SAR图像海洋背景和舰船目标特点,文献[1]提出了基于小波多分辨率分析的卫星SAR海洋图像舰船目标检测的新方法。在此基础上,本文针对不同海情杂波服从不同概率密度分布的特点,讨论了复杂杂波背景下基于小波变换检测海洋SAR图像中舰船目标的性能,给出了不同海情下的检测性能,并与传统门限检测方法比较,给出了不同信杂比下虚警概率曲线。仿真结果表明,该方法实用、有效。     

一种新的极化SAR图像目标CFAR检测方法

该文提出了一种新的极化SAR图像目标CFAR检测算法。首先,在乘积模型框架下,引入具有均匀度变化下广泛杂波区域建模能力的逆Gamma分布,推导出了极化匹配滤波(PMF)检测量的分布模型P-G0分布。进而,利用基于Mellin变换的对数累积量导出了P-G0分布的参数估计器,保证了PMF检测量的精确建模。最后,推导出P-G0分布的CFAR检测阈值求解公式,以此设计了新的CFAR检测算法。利用RADARSAT-2极化SAR数据的实验结果表明了P-G0分布对不同均匀度的地物都具有良好的拟合性能,所提检测算法能够实现均匀度变化较大环境下目标的准确、自动检测。     

融合极化旋转域特征和超像素技术的极化SAR舰船检测

对海监视是极化SAR的重要应用,密集区域的舰船目标检测是当前面临的主要挑战之一。舰船密集区域受多目标串扰,传统的恒虚警率(CFAR)检测滑窗难以选取纯净的海杂波样本用于确定检测门限,将导致检测性能下降。针对这一问题,该文从特征提取和检测器设计两方面出发,提出一种融合极化旋转域特征和超像素技术的极化SAR舰船检测方法。在特征提取方面,雷达目标的后向散射敏感于目标姿态与雷达视线的相对几何关系,由此带来的散射多样性隐含信息可通过极化旋转域分析进行挖掘。该文利用极化相关方向图及导出的一系列极化旋转域特征,根据目标杂波比(TCR)分析,优选TCR最高的3个极化特征量用于构建目标检测器。在此基础上,该文在检测器设计方面提出了一种基于K均值聚类的杂波超像素筛选方法,有效避免了密集区域舰船目标对邻近杂波的影响,基于筛选的杂波像素点得到舰船目标CFAR检测结果。基于Radarsat-2和高分三号星载全极化SAR数据的对比实验表明,所提方法能有效实现密集区域舰船目标检测,检测品质因数达到95%。      

基于分数低阶本征滤波的雷达目标检测方法

在非高斯相关杂波背景下,通常杂波分布的概率密度函数结构复杂甚至无闭式表达,难以建立统计检测模型。针对此问题,以α稳定分布为背景,基于分数低阶统计量和最佳滤波器理论,以滤波器输出分数低阶信杂比最大为准则,给出了一种分数低阶本征滤波(FLOEF)模型。该模型利用杂波的分数低阶协方差矩阵对非高斯相关杂波进行白化,可显著改善信杂比,实现非高斯相关杂波背景下雷达目标的有效检测。通过仿真和实测数据给出了FLOEF在不同条件下的检测性能,并同传统基于二阶统计量的本征滤波进行了比较,结果验证了FLOEF的优越性。     

基于X波段无人机暗室测量数据的雷达探测性能分析

雷达对无人机的探测性能分析可为低空监视雷达设计、对无人机探测与反制提供重要参考。基于X波段固定翼无人机的全极化暗室实测数据，采用零记忆非线性变换（ZMNL）法生成相关Weibull杂波，分析了平方律检测器对无人机的检测概率，并根据仿真获得的检测所需最小信杂比，借助地杂波背景下的雷达距离方程计算了全极化雷达对低空无人机的探测距离。结果表明：考虑到无人机起伏损失和地杂波强度在不同极化方式之间的差异，VV极化的探测距离最远，交叉极化次之，而HH极化的综合表现相对较差。     

适合P波段UWBSAR的改进PGA算法

相位梯度自聚焦（PGA）算法是一种高效的自聚焦算法，其优良的聚焦性能是建立在合理的统计模型和高信杂比的基础上。对于信杂比较低的P波段超宽带合成孔径雷达（UWBSAR）图像来说，传统PGA算法的聚焦效果并不理想。对此，文中提出了将对比度准则和传统PGA算法相结合的改进PGA算法，有效地削弱了图像信杂比对PGA聚焦性能的影响。文中还提出了“参考点对齐”法来校正PGA算法聚焦所产生的图像平移（沿方位向）失真问题。文中所提算法已经成功运用于某P波段UWBSAR实测图像数据的聚焦处理，并得到了比传统PGA算法更好的聚焦处理结果。     

极化SAR图像的联合加权极化差异度变化检测算法

以联合加权极化差异度为特征提出了一种极化SAR图像变化检测新算法。首先计算两个不同时相的极化SAR图像的极化散射差异度和极化功率差异度,然后根据二者的相对大小分配加权系数计算联合加权极化差异度,得到差异图像,最后通过阈值分割技术提取变化区域实现变化检测。利用两组美国UAVSAR系统采集的全极化SAR实测数据对算法进行了验证,结果表明,本文算法能有效检测地物的变化情况,且检测虚警少、轮廓清晰。除此之外,算法无需知道图像的统计分布,通用性强。      

一种面向对象的时间序列极化SAR图像变化检测方法

提出了一种改进的时间序列极化合成孔径雷达(SAR)图像变化检测方法。该方法首先使用简单线性迭代聚类算法对时间序列极化SAR图像进行过分割,得到时间序列图像的超像素一致表达。然后对各个超像素进行时间序列上的任意双时相交叉变化检测,得到包含时间序列图像变化信息的变化检测矩阵。最后根据变化信息对图像在时间序列上进行平滑滤波并生成动态变化图,对地物变化情况进行分析。实验结果表明,文中方法能够有效地检测和分析时间序列极化SAR图像的变化情况。     

An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The $G^{0}$ distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability.      

Analysis of sea spikes in radar sea clutter data

In this paper three sets of high-resolution, coherent, and polarimetric radar sea clutter data are analyzed and compared with radar sea clutter models. The nature of the data allows a thorough analysis of the power, polarization and velocity of the sea clutter. It is shown that these quantities, especially the velocity, are good measures of many physical properties of the ocean surface. Furthermore, it is shown that these physical properties match well with the sea clutter models. Sea clutter is found to consist of two components, a diffuse background, characterized by low values of backscattered power, HH/VV polarization ratio and Doppler velocity, and a number of spiking events, which possess higher power, polarization ratio and velocity. The background is reasonably well modeled by tilt-modulated Bragg scattering, whereas the spikes may be associated with the scattering on steepened and/or breaking waves. Moreover, it is shown that the influence of microbreakers has to be taken into account to explain the relatively high polarization ratio. A breaking wave origin for the spikes is supported in two ways. First, by a detailed analysis of the temporal behavior of individual spike backscatter properties, and second, by a statistical analysis of the entire population of spikes.     

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.     

全卷积神经网络应用于SAR目标检测

在合成孔径雷达（SAR）图像目标检测中,由于场景杂波的复杂多变,对背景杂波统计模型估计难度增加,从而导致多数检测器容易受到背景杂波的干扰。针对如何避免场景杂波对目标检测干扰的问题,提出了一种基于全卷积神经网络的SAR目标检测模型。该模型将目标检测任务转化为像素分类问题,利用卷积神经网络对数据集中目标像素特征和背景杂波像素的先验信息进行自主学习,有效减少了虚警目标的数量;通过对目标及其阴影区域的联合检测,提高了目标的检测概率。对多个不同场景图像进行测试,实验结果表明提出的检测模型具有良好的检测性能和鲁棒性能,与传统恒虚警检测算法相比,在无需考虑背景杂波统计模型前提下有效降低了虚警概率。     

基于Toeplitz矩阵特征值分解的SAR图像舰船目标检测方法

合成孔径雷达（SAR）图像舰船目标检测一直是海洋监测领域的重要手段。经典的恒虚警率（CFAR）检测依赖于分布模型及多参数的准确估计,难以适应复杂多变的海面背景。新兴的信息几何舰船检测方法挖掘了目标与杂波的统计差异,实现舰船的显著性表示,但依然受限于背景杂波的精确建模。考虑到现有方法的局限性,本文提出了一种基于Toeplitz矩阵特征值分解的SAR图像舰船目标检测算法。在无需寻求背景杂波分布模型的前提下,通过构建Toeplitz矩阵,以其特征值均值为检验统计量,充分获取目标与背景杂波的差异。在高分三号卫星和TerraSAR-X卫星实测SAR图像上的实验结果证明,相比于现有的多种典型方法,本文方法取得了更优的检测性能与更快的计算速度。      

极化SAR动目标定位中的残差图像干涉相干性分析

该文研究极化SAR动目标检测及定位中的频域残差图像干涉相干性优化问题.给出了多普勒频域杂波对消处理的基本流程,提出了一种利用频域残差图像进行最优极化干涉处理从而提高目标运动参数估计精度的方法.结合最优极化干涉理论,通过仿真分析研究了不同极化下频域残差图像的干涉相干性,验证了文中方法的有效性.     

Statistical and spatial properties of forest clutter measured withpolarimetric synthetic aperture radar (SAR)

The purpose of this study was to assess the characteristics of the polarimetric radar clutter collected over various types of vegetation, and to evaluate the potential utility of various measures for classifying vegetated terrain. It utilized airborne radar data collected in October 1988 of pure and mixed stands of jack pine and aspen located in central Minnesota, and data collected in August 1989 of forested areas surrounding Durham, NC. The measured parameters are the polarimetric signature, a statistical distribution shape parameter, and the texture autocorrelation function. A method is presented for calculating the texture autocorrelation from single-look complex SAR imagery. Comparison of parameters from different clutter types at a variety of frequency, polarization, and incidence angle combinations showed that different terrain types exhibit unique polarimetric, statistical, and spatial properties     

高分辨SAR图像中杂波的统计特性分析

为使SAR图像目标检测在复杂多变的地物杂波环境中获得最佳的性能,研究SAR图像中杂波的统计特性将是一件有意义的工作.在综合对比已有统计模型的基础上,从理论上重点分析了G0分布.运用实测SAR场景中表征不同地物类型的大量数据,从幅度直方图拟合、拟合精度检验等方面深入分析了杂波的统计特性.得出了在目前已有实用的统计模型中,C0分布最适合于描述SAR图像杂波统计特性,它是一种对于均匀、一般不均匀和极不均匀杂波区域建模的通用分布的新结论.因此,以C0分布为杂波模型的目标检测算法具有巨大的应用潜力.     

信号层融合在慢动小目标检测中的应用

针对海上慢动小目标检测背景，提出了一种基于信号层融合的多雷达融合检测方法，并对实际应用中需要解决的问题进行了分析讨论。该方法利用目标和海杂波在不同频段下的统计性能差异，对多个雷达的观测信息在检测前进行融合，然后在相关域进行积累检测，与传统的检测方法相比，能够显著改善信号的信杂比，提高目标检测概率。     

非高斯相关杂波背景下雷达目标检测方法研究

在非高斯相关杂波背景下,基于MTD(Moving Target Detection)的雷达目标检测性能严重下降。针对该问题,根据Alpha稳定分布杂波模型、分数低阶统计量理论,以输出信杂比最大为准则,提出了一种适用于非高斯相关杂波背景的雷达目标检测方法。该方法通过分解信号分数低阶协方差矩阵,计算等效杂波分数低阶协方差矩阵特征向量,得到最佳滤波器系数。通过仿真和实测数据,对所提出方法的检测性能进行了验证,并且与基于MTD的检测方法进行了比较,结果表明,在非高斯相关杂波背景下,所提方法的检测性能明显优于传统的MTD。