残差点退化的统计费用网络流机载相位解缠算法

目的 相位解缠是InSAR干涉数据处理的关键步骤,而解缠不连续（即相位跳变）问题却普遍存在,尤其在机载InSAR系统中,由于数据的高分辨率,使得低矮地物如树木带在数据中表现为相位不一致,因而相位跳变问题更加显著。星载InSAR相位解缠广泛使用统计费用网络流（SNAPHU）算法^[1],借鉴其经验将SNAPHU算法引入高分辨机载InSAR相位解缠。而残差点退化方法能有效补偿局部相位不一致区域。因此本文提出一种结合残差点退化方法与SNAPHU算法的高分辨率机载InSAR相位解缠算法。方法 将原始InSAR数据滤波且去除平地相位,再对其进行残差点退化处理。残差点退化包含残差点定位,及残差点补偿两部分。根据残差点及其邻域像元的性质,对残差点进行补偿使其退化为非残差点,不断迭代这一过程,以减少图像中的残差点,优化局部数据。根据机载InSAR系统定标参数,修正SNAPHU算法中的参数及几何模型,使用修正后算法进行相位解缠。结果 利用2011年四川江油地区的单轨双天线X波段机载InSAR数据进行了试验,试验结果表明,在相位不一致,相干性低的连续树木带区域,该算法显著缩小了解缠相位不连续区域,修正了大面积的相位跳变。结论 验证了残差点退化方法结合统计费用网络流算法可有效解决解缠相位大面积跳变问题,且对噪声具有鲁棒性。     

质量图和残差点相结合的InSAR相位解缠方法

在分析质量图原理及其在相位解缠中应用的基础上,提出了一种质量图和残差点相结合的InSAR干涉图相位解缠算法,算法以干涉图中残差点的分布信息为依据,优化质量阈值的确定方法,并以此为依据将干涉相位图划分为高低质量区域,指导相位解缠的顺利进行。通过使用不同质量图的实验结果表明,该方法减少了干涉图中有效信息的流失,并且指标明确,人工干预少,解缠结果较枝切法等经典方法更准确而有效。     

基于中性残差点集的干涉SAR相位解缠算法

相位解缠是合成孔径雷达干涉测量数据处理的关键性步骤之一.在分析Goldstein枝切法和残差点位置的基础上,提出了一种改进的相位解缠算法.首先采用4点环路积分识别残差点,在邻近偶极子对残差预处理后,将剩下所有残差点分成若干个总电荷平衡的中性点集.然后采用普里姆算法对各中性残差点集依次处理,获得相应的最小代价生成树,全部最小代价生成树的边就是相位解缠所需的枝切线.最后,采用真实干涉SAR数据,分别利用本文方法和Goldstein法做了相位解缠实验.通过枝切线总长度和未解缠象素数量两项性能指标对比,证明了改进算法的有效性.     

一种机载InSAR水体阴影的提取和识别方法

为了解决机载InSAR DEM中水体和阴影区域质量不佳需要区分修复的问题,提出一种综合利用机载InSAR数据源自动提取水体和阴影并加以识别的方法。首先基于InSAR DEM进行粗差点检测,利用粗差点作为种子点在SAR图像中区域生长,提取完整的水体和阴影区域;然后利用沿斜距向高程差和雷达俯角构造约束条件自动识别两者。通过对实测的机载高分辨率InSAR数据进行处理,水体阴影的识别率达到92%以上,其中水体和地形阴影的识别较好,而受制于DEM内在噪声等因素的影响,由树木造成的小块阴影容易造成误分。     

数字全变差向量滤波与相位解缠

分析了"噪声"对合成孔径雷达干涉(InSAR)测量中相位解缠的影响,介绍了SAR影像相干斑噪声的固有性、特殊性以及通常的滤波处理方法.基于连续的各向异性扩散偏微分方程图象处理模型,提出适合于干涉图(mterferogram)和干涉相位条纹图(phase fringe)数字全变差滤波(DTVF)模型.计算分析表明滤波处理极大减少了干涉相位条纹图的残差点,降低设置枝切线的复杂性,在保证解缠质量前提下,提高了大幅图象相位解缠可行性.     

基于ALOS极化干涉SAR数据的DEM提取方法研究

极化干涉理论的发展以及全极化卫星的发射,为极化干涉的研究应用提供了广阔的发展空间,开展极化干涉SAR提取DEM的研究对于解决单极化干涉SAR在植被覆盖区DEM提取精度较低的问题具有重要意义。基于山东泰安地区的一对ALOS PALSAR全极化干涉数据,利用相干性最优方法和最大化相位中心分离的方法获取极化干涉信息,并通过对优化后干涉信息的滤波、解缠、基线精确估计等处理来提取DEM,最后将相干优化的结果与传统HH、HV、VV单极化干涉结果进行对比分析。结果表明:优化方法较单极化干涉方法可以有效降低干涉图的噪声,减少相位解缠的残差点,提高解缠相位的质量,并且不同优化方法之间还存在一定差异,数值半径方法获取的DEM精度要好于其他两种优化方法;通过等值线-Goldstein二级干涉SAR滤波方法可进一步提高单极化和优化干涉图的质量,降低残差点,提高相位质量,在水体覆盖的区域通过滤波窗口的设置可以很好地改善DEM的精度。     

基于随机并行梯度下降算法的InSAR相位解缠方法

相位解缠是干涉合成孔径雷达InSAR数据处理中的一个关键步骤,解缠结果的好坏直接影响最终数字高程模型的精度。介绍了一种基于随机并行梯度下降SPGD算法的解缠方法,该方法对图像中各相位点施加随机并行扰动,通过迭代使得解缠误差代价函数收敛到全局最优值,从而实现相位解缠的目的。模拟和实测数据实验结果表明,相较于最小二乘解缠方法,随机并行梯度下降解缠算法精度更高,且原理简单,易于实现,为相位解缠提供了一个全新的思路。     

相位解缠枝切法中的枝切线设置的优化

在合成孔径雷达干涉测量中,相位解缠是处理insar数据的关键步骤之一.Glodstein枝切法是较为经典的一种相位解缠方法,思路明确实现简单,是其他积分路径方法的基础和依据,但是在枝切线的设置方法上存在很多的分歧,并且形成的枝切线容易形成大范围的封闭区域,影响正常解缠.本文就是通过对残差点的特点深入分析.在如实地还原了Glodstein枝切法的基础上.并对其算法中的"最大窗口"和"查找顺序"等问题提出了合理的解决方法,通过连接偶板子,增加了算法的预处理,最终优化了枝切线设置,从而达到更良好的解缠效果.     

InSAR相位解缠算法的实验对比研究

相位解缠是InSAR处理中的一个关键步骤,相位解缠算法的选取很大程度上影响着最终的结果。本文主要介绍和比较了6种常用的相位解缠算法,并选取西藏当雄地区的地震同震影像进行实验分析,对解缠结果的质量进行评价比较。结果表明:统计耗费网络流算法结果充分顾及了相干图所包含的信息,获得了一个较优的全局解,解缠结果的连续性较好。而且直接处理感兴趣的且数据质量好的离散区域,实现效率高,可以将误差限制在一个小范围内,防止误差的再传递,解缠结果较精确。     

相位分块与拟合法结合的InSAR相位解缠算法

为进一步提高InSAR干涉图的解缠效果,提出了针对InSAR干涉图的相位分块与拟合法结合的相位解缠算法。该算法将获得的相位图分为多个相位区间块,块内相位值都在给定的相位区间内,将像素个数大于等于给定阈值的块归类为正常块,小于给定阈值的块归类为残余像素块;然后利用拟合法依次进行正常块间的相位解缠绕和残余像素的相位解缠绕,通过合并解缠后的块得到最终的解缠结果。为验证算法的适用性,采用模拟数据和实测数据进行实验处理,以均方根误差和算法运行时间作为评价指标,将此算法与传统的Goldstein枝切法、质量图引导法、四向剪切最小二乘法进行比较。结果表明,本文算法的噪声鲁棒性更好,解缠结果更为准确。     

Performance analysis of a novel time-domain real-time SAR processor

We test the performance of a new phase-preserving time-domain signum coded SAR processor (SCSP) aimed at real time operations. Raw signal interferometric pairs relevant to the Shuttle Radar Topography X-band Mission are simulated. A full result comparison between SCSP and conventional interferometric products is presented by using simulated canonical and real scenarios. The simulated canonical scene consists of a pyramid and three corner reflectors. The simulated real scene refers to the Mt Etna area in Sicily, Italy. Raw data are simulated both for ascending and descending orbits. Results show that SCSP combined with an iterative phase unwrapping algorithm can generate digital elevation models with good accuracy in spite of the higher phase noise level.     

Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing

Three-dimensional (3D) synthetic aperture radar (SAR) imaging via multiple-pass processing is an extension of interferometric SAR imaging. It exploits more than two flight passes to achieve a desired resolution in elevation. In this paper, a novel approach is developed to reconstruct a 3D space-borne SAR image with multiple-pass processing. It involves image registration, phase correction and elevational imaging. An image model matching is developed for multiple image registration, an eigenvector method is proposed for the phase correction and the elevational imaging is conducted using a Fourier transform or a super-resolution method for enhancement of elevational resolution. 3D SAR images are obtained by processing simulated data and real data from the first European Remote Sensing satellite (ERS-1) with the proposed approaches.     

A new phase unwrapping method based on region recognition and region expansion

Phase unwrapping is a key step in retrieving digital elevation models (DEMs) from across-track interferometric synthetic aperture radar (InSAR) data. The coherence of synthetic aperture radar (SAR) data set is an effective indicator for the quality of phase unwrapping. However, the coherence of different regions usually distributes unevenly in SAR images monitoring heterogeneous areas. Errors in low-coherence areas are prone to pollute the whole image. In order to mitigate propagation error, a new phase unwrapping algorithm based on region recognition and region expansion is proposed. In the region recognition step, optical images are incorporated to recognize low-coherence regions by virtue of supervised classification technique. Low-coherence regions and the ones that are not of interest for the application are then discarded. In the region expansion step, stable pixels of high coherence are selected as growing seeds, and then phase unwrapping grows from high-quality regions to low-quality ones guided by coherence information and weighted numbers of neighbouring unwrapped pixels. The ambiguity number of a wrapped pixel is estimated from its neighbouring pixels under the criteria of pixel distance and phase gradient. Iterative examination continues until the whole image is unwrapped. Experiments on PALSAR and ASAR data demonstrate its validity and advantages over other classical methods.     

Multi-objective evolutionary for synthetic aperture radar image segmentation with non-local means denoising

Synthetic aperture radar (SAR) image segmentation is an important problem of the realm of image segmentation. In this study, a novel SAR image segmentation algorithm using a multi-objective evolutionary algorithm based on decomposition with non-local means denoising (MISD) is proposed. The novelty of MISD lies in the following issues: (1) an effective multi-objective method with decomposition to solve SAR image segmentation; (2) in order to denoise the SAR images and retain the details, we employ non-local means to remove the noise. The multi-objective decomposition method makes MISD have lower computational complexity. In order to evaluate the performance of the new method, we compared the results with three other popular segmentation approaches on four simulated and two real SAR images. In our experiments, the new method can always find better results, which means MISD is a promising SAR image segmentation method.     

SAR despeckling neural network with logarithmic convolutional product model

ABSTRACT

Synthetic aperture radar (SAR) images are inevitably contaminated by speckle noise due to its coherent imaging mechanism. Speckle noise obscures the intrinsic radar cross section (RCS) information in SAR images. This article proposes a novel deep neural network architecture specifically designed for despeckling purpose. It uses a convolutional neural network to extract image features and reconstruct a discrete RCS probability density function (PDF). It is trained by a hybrid loss function which measures the distance between the actual SAR image intensity PDF and the estimated one which is derived from convolution between the reconstructed RCS PDF and prior speckle PDF. The network can be trained by either purely simulated image patches or real SAR images. Experiment results on both simulated SAR images and real NASA/JPL AIRSAR images are used to test the performance, and the results show the efficacy of the proposed despeckling neural network compared with three state-of-the-art filters.     

基于谱归一化生成对抗网络的目标SAR图像仿真方法

为解决合成孔径雷达(Synthetic Aperture Radar, SAR)自动目标识别(Automatic Target Recognition, ATR)中的数据稀疏问题,提出一种基于谱归一化生成对抗网络(Spectral Normalization Generative Adversarial Network, SN-GAN)的目标SAR图像仿真方法。本文方法通过构建目标—场景—雷达耦合物理模型,求解散射强度分布图,利用SN-GAN实现对散射强度分布图的优化,生成高质量仿真SAR图像。通过3种相似性评估算法对仿真图像进行相似度评估,验证本文仿真方法的有效性。最后通过多组SAR ATR进行实验验证,在训练集中加入SN-GAN优化的仿真SAR图像可以有效缓解数据稀疏问题,提升分类算法的准确率。     

A novel multi-scale level set method for SAR image segmentation based on a statistical model

To overcome the problems of large data volumes and strong speckle noise in synthetic aperture radar (SAR) images, a multi-scale level set approach for SAR image segmentation is proposed in this article. Because the multi-scale analysis of SAR images preserves their highest resolution features while additionally making use of sets of images at lower resolutions to improve specific functions, the proposed method is useful for removing the influence of speckle and, at the same time, preserving important structural information. The Gamma distribution is one of the most commonly used models employed to represent the statistical characteristics of speckle noise in a SAR image and it is introduced to define the energy functional. Moreover, based on the multi-scale level set framework, an improved multi-layer approach is introduced for multi-region segmentation. To obtain a fast and more accurate result, a novel threshold segmentation result is used to represent the initial segmentation curve. The experiments with synthetic and real SAR images demonstrate the effectiveness of the new method.     

Sparse SAR imaging based on L1/2 regularization

In this paper,a novel method for synthetic aperture radar(SAR)imaging is proposed.The approach is based on L1/2 regularization to reconstruct the scattering field,which optimizes a quadratic error term of the SAR observation process subject to the interested scene sparsity.Compared to the conventional SAR imaging technique,the new method implements SAR imaging effectively at much lower sampling rate than the Nyquist rate,and produces high-quality images with reduced sidelobes and increased resolution.Also,over the prevalent greedy pursuit and L1 regularization based SAR imaging methods,there are remarkable performance improvements of the new method.On one hand,the new method significantly reduces the number of measurements needed for reconstruction,as supported by a phase transition diagram study.On the other hand,the new method is more robust to the observation noise.These fundamental properties of the new method are supported and demonstrated both by simulations and real SAR data experiments.     

An estimation method for InSAR interferometric phase combined with image auto-coregistration

1 Introduction Synthetic aperture radar interferometry (InSAR) is an important remote sensing tech- nique to retrieve the terrain digital elevation model (DEM)[1,2]. Image coregistration and interferometric phase unwrapping are two key processing procedur…