Sparse representation-based algorithm for joint SAR image formation and autofocus

Inaccuracies in the observation model of the synthetic aperture radar (SAR) due to inaccuracies of the velocity and position of the platform or atmospheric turbulence cause degradations in reconstructed images which necessitate the use of autofocus algorithms. In this paper we propose a novel signal processing algorithm for joint SAR image formation and autofocus in a synthesis dictionary based sparse representation framework. Proposed algorithm can be applied broadly to scenes that exhibit sparsity with respect to any dictionary. This is done by extending our previously developed sparse representation-based SAR imaging framework to joint SAR image formation and autofocus. To this end, the phase error vector is separated from the unknown phase of the complex-valued back-scattered field. Phase error vector is estimated using a MAP estimator and compensated through an iterative algorithm to produce focused images. We demonstrate the effectiveness of the proposed approach on synthetic and real imagery.     

Real-time motion compensation, image formation and image enhancement of moving targets in ISAR and SAR using S-methodbased approach

The commonly used technique for inverse synthetic aperture radar (ISAR)/synthetic aperture radar signal analysis is a two-dimensional Fourier transform (FT), which results in an image of the target's reflectivity mapped onto a range and cross-range plane. However, in cases where the line-of-sight projections of the target's point velocities change or there is uncompensated movement within the coherent integration time, the FT produces blurred images. For target recognition applications, mainly those in military surveillance and reconnaissance operations, a blurred ISAR image has to be refocused quickly so that it can be used for real-time target identification. Two standard techniques used for improvement of blurred ISAR images are motion compensation and the use of quadratic time-frequency representations. Both are computationally intensive. The authors present an effective quadratic time-frequency representation, the S-method. This approach performs better than the Fourier transform method by drastically improving images of fast manoeuvring targets and by increasing the SNR in both low and high noise environments. These advantages are a result of the S-method's ability to automatically compensate for quadratic and all even higher-order phase terms. Thus, targets with constant acceleration will undergo full motion compensation and their point scatterers will each be localised. It should be noted that the source of the quadratic term can come not only from acceleration, but also from non-uniform rotational motion and the cosine term in wide-angle imaging. The method is also computationally simple, requiring only slight modifications to the existing FT-based algorithm. The effectiveness of the S-method is demonstrated through application to simulated and experimental data sets.     

应用DBS技术在SAR/GMTI模式下高分辨率成像

多普勒波束锐化成像技术(DBS)能为检测出的动目标定位提供参考，从而有效地实现精确打击。DBS技术在弹道导航、地形匹配等许多方面有着广泛的应用。首先简述了DBS成像方法的一般工作原理，经分析得出结论：DBS成像在SAR，GMTI模式下应用时，如果引入聚焦型的SAR处理方法．即通过去距离单元走动、去方位向调频斜率等方法可有效提高雷达成像质量。最后利用雷达实测数据进行处理．得出了较满意的高分辨DBS成像结果。     

面向SAR解译的OpenSAR数据开放共享平台

大量带有真值标注的数据集是SAR图像解译研究的重要基础和支撑。在推动SAR图像解译研究发展面临的诸多困难之中,缺少一个开放共享的数据集用于解译算法的研究和公平测试比较,是其中一个非常重要的因素。文中主要介绍构建的国内第一个面向SAR图像解译的数据开放共享在线平台:OpenSAR。详细介绍了OpenSAR平台的设计思想、功能组成、关键技术和已有数据集。通过该平台的构建,对于SAR解译研究能够取得良好的促进作用。     

SAR图像稀疏对抗攻击

基于深度学习的图像解译技术在多个领域都取得了巨大成功,在合成孔径雷达（Synthetic Aperture Radar,SAR）图像分类、检测、分割等问题中也逐渐开始广泛应用。现有的SAR图像分类深度学习模型由于训练数据集样本量较小易过拟合,样本的微小改变易导致模型分类错误,产生对抗攻击现象。针对上述问题,本文从攻击方法、攻击结果和攻击目标三方面说明了SAR图像对抗攻击存在的问题和挑战。本文聚焦SAR图像的稀疏性,具体阐述了稀疏攻击提出背景和SAR图像中稀疏性的表现形式,并就常见稀疏攻击方法进行分析总结。文章在MSTAR数据集上验证了现有的稀疏攻击方法的有效性,分析了算法计算效率和成功率、耗时等指标,并对SAR图像分类稀疏对抗攻击方法进行展望。      

SAR图像滤波的迭代方法

对合成孔径雷达图像迭代滤波处理的效果及其问题进行了分析，并结合统计滤波方法和小波分析方法，提出了一种基于细节补偿的MAP（BDC MAP）迭代滤波算法。实验结果表明，新的迭代滤波算法较好地解决了去噪和细节保留之间的矛盾，而且算法稳定，收敛很好。     

SUPPRESSING THE COHERENT SPECKLE OF SAR IMAGES WITH WAVELET ANALYSIS

This paper studies Synthetic Aperture Radar (SAR) images contaminated by the coherent speckle noise with the multiresolution analysis of wavelet transform. This study shows that the influences of the speckle on different frequency components of the SAR image are different, and that the SAR image and the speckle have different manners of singularity. So, this paper presents a denoising method of wavelet analysis to reduce the speckle. Some experiments approve that this method not only suppresses the speckle effectively, but also preserves as much target characteristics of the original image as possible. It shows that this denoising method of wavelet analysis offers a very attractive alternative to suppress the coherent speckle noise of the SAR image.     

Multi-region segmentation of SAR image by a multiphase level set approach

In this letter,a multiphase level set approach unifying region and boundary-based information for multi-region segmentation of Synthetic Aperture Radar（SAR）image is presented.An energy functional that is applicable for SAR image segmentation is defined.It consists of two terms describing the local statistic characteristics and the gradient characteristics of SAR image respectively.A multiphase level set model that explicitly describes the different regions in one image is proposed.The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by level set but also to guarantee the accuracy of segmentation.According to the presented multiphase model,the curve evolution equations with respect to edge curves are deduced.The multi-region segmentation is implemented by the numeric solution of the partial differential equations.The performance of the approach is verified by both simulation and real SAR images.The experiments show that the proposed algorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy,thus correctly divides the multi-region SAR image into different homogenous regions.     

极化雷达图像目标相干系数判据的检测研究

由理想目标散射矢量与待识目标散射矢量的相干系数判据,对待识目标进行检测与同类识别. 利用复旦大学电磁波信息科学教育部重点实验室的散射计算软件BART进行理想目标极化散射计算,构造目标散射矢量,并进行合成孔径雷达（Synthetic Aperture Radar,SAR)成像模拟. 以此目标成像模拟数据作为目标识别样本,对全极化系统Pi-SAR观测海面养殖箱的成像数据,研究其相干系数判据的检测与识别. 结果表明该方法是有效的.     

极化雷达图像目标相干系数判据的检测研究

由理想目标散射矢量与待识目标散射矢量的相干系数判据,对待识目标进行检测与同类识别. 利用复旦大学电磁波信息科学教育部重点实验室的散射计算软件BART进行理想目标极化散射计算,构造目标散射矢量,并进行合成孔径雷达（Synthetic Aperture Radar,SAR)成像模拟. 以此目标成像模拟数据作为目标识别样本,对全极化系统Pi-SAR观测海面养殖箱的成像数据,研究其相干系数判据的检测与识别. 结果表明该方法是有效的.       

基于混合Gamma建模与MRF的SAR图像分割方法

文中基于结合利用SAR图像统计信息和像素的空间约束信息的思路,提出了基于混合Gamma建模和MRF的分割方法,算法中利用混合Gamma模型对SAR图像进行统计建模,利用MRF模型对像素空间相关性进行建模。文中验证了混合Gamma模型参数估计的准确性以及自适应估计分割类别数目的有效性。最后用模拟SAR数据和实测SAR数据验证了文中所提出的分割算法的有效性。     

SAR image autofocus by sharpness optimization: a theoretical study.

Synthetic aperture radar (SAR) autofocus techniques that optimize sharpness metrics can produce excellent restorations in comparison with conventional autofocus approaches. To help formalize the understanding of metric-based SAR autofocus methods, and to gain more insight into their performance, we present a theoretical analysis of these techniques using simple image models. Specifically, we consider the intensity-squared metric, and a dominant point-targets image model, and derive expressions for the resulting objective function. We examine the conditions under which the perfectly focused image models correspond to stationary points of the objective function. A key contribution is that we demonstrate formally, for the specific case of intensity-squared minimization autofocus, the mechanism by which metric-based methods utilize the multichannel defocusing model of SAR autofocus to enforce the stationary point property for multiple image columns. Furthermore, our analysis shows that the objective function has a special separble property through which it can be well approximated locally by a sum of 1-D functions of each phase error component. This allows fast performance through solving a sequence of 1-D optimization problems for each phase component simultaneously. Simulation results using the proposed models and actual SAR imagery confirm that the analysis extends well to realistic situations.     

用小波变换抑制SAR图像中的斑点噪声

抑制合成孔径雷达图像中的斑点噪声一直是处理图像并得到准确图像信息的难点，提出了一种基于小波变换抑制合成孔径雷达（SAR）图像中的斑点噪声的方法，对原有的小波变换方法作了改进，能更好地保留图像的边缘信息，并能简化计算量。在仿真实验中使用了合成的模拟图像和真实的合成孔径雷图像，并与以往的小波去噪滤波方法以及一些经典的斑点噪声滤波方法（包括中值滤波，Lee滤波，Frost滤波）进行比较，在综合考虑了滤波算法在均匀区域对斑点噪声的抑制能力以及保留边缘信息能力的情况下，提出的算法有更好的效果。     

基于图像域的势函数正则化SAR车辆图像增强算法

SAR图像增强技术是SAR信号处理中很重要的一门技术。一幅SAR原始图像中除了包含模糊的目标区域和阴影区外还包含了大量的噪声。在目标识别过程中,如果先对原始SAR图像进行增强处理,降低噪声的同时增强模糊的目标区域和阴影区,然后进行特征提取,就可以大大降低识别系统的运算量,提高识别效率。正则化方法广泛应用于图像分割,图像增强等领域。通过对两种正则化算法的比较,结合两种方法的优点,提出了一种改进的基于图像域的势函数正则化图像增强算法。利用MSTAR数据库数据进行仿真试验,取得了良好的效果。     

LFM信号误差对SAR成像的影响分析

该文讨论了线性调频信号频率线性度的定义,分析了线性调频信号数字产生过程中,频率线性度、相位误差、幅频特性不平坦产生的原因以及对SAR成像的影响。给出了计算机仿真结果。     

SAR成像质量指标评估软件系统的设计

合成孔径雷达(SAR,Synthetic Aperture Radar)是一种应用广泛的微波遥感成像雷达,能够全天时、全天候对地观测成像,并且图像有着高方位分辨率。SAR图像研究是遥感方向最为重要的前沿领域之一。目前没有一套专门的软件系统工具来对SAR成像进行指标的计算与测量,都是建立在通用的辅助工具如MATLAB上,计算效率低且不方便。文中设计了一种SAR成像的质量评估系统软件,该系统中包含一套比较完备的SAR成像质量指标,然后以这套指标为主要出发点,设计开发出SAR图像质量指标评价的软件。该软件以C/C++语言为基础,以OGRE和CEGUI等开源库为框架。该系统能够实现对数据文件的读取、图像的基本操作、图像和数据的质量指标测定、指标的测定和计算等,并且有着友好的人机交互功能。     

全极化合成孔径雷达多视图像的极化特征分析

该文基于乘性相干斑模型,对全极化合成孔径雷达多视图像的极化特征参数(同极化比、交叉极化比和同极化相位差)进行了分析,利用实际的模拟数据对特征参数的PDF进行了拟合测试,分析了多视极化特征参数的统计特性对极化相干斑抑制算法的影响。利用极化比和相位差的特点,对地物进行分类,所得分类精度与利用最优极化分类法的分类精度具有可比性。     

合成孔径雷达图像中桥梁形状的目标识别

识别 SAR图像中桥梁形状目标 ,先对 SAR图像进行预处理 ,排除明显的非目标区 ,增强对目标的识别效果 ,然后在此基础上进行针对桥梁形状目标的处理 ,包括方向比值法和双边参数法 ,可得到较好的识别效果。     

Calibration of a cross-polarized SAR image using dihedral cornerreflectors

An X-band cross-polarized synthetic aperture radar (SAR) image taken from an aircraft has been absolutely calibrated using 45°-inclined dihedral corner reflectors with an aperture dimension of 15 cm×25 cm. The experiment was carried out during an airborne SAR campaign (called the SAR-580 Experiment) conducted by the National Space Development Agency of Japan in 1983. The experimental procedure, the characteristics of the corner reflector, and the results of the experiment are described     

基于四阶矩的SAR图像目标检测

SAR图像中车辆目标检测是一个备受瞩目的关注点,运用传统的低阶矩方法进行SAR图像中目标检测,尤其是在图像中含有乘性噪声和在分辨率降低时,普遍存在检测率不高,不能充分体现出目标相对背景杂波的特性。本文根据SAR图像中目标的特性,提出基于四阶矩的方法对SAR图像进行目标检测。实验表明,采用四阶矩方法后,在乘性噪声和分辨率降低状况下,四阶矩方法的检测概率相对传统的二阶矩方法有明显提高。未来四阶矩将更广泛地用于SAR图像中对目标进行检测,有重大的应用前景。