Investigation of the ground displacement in Saint Petersburg,Russia, using multiple-track differential synthetic aperture radar interferometry

Global sea level rise and local land subsidence might exacerbate the risk of flooding in coastal plains. Among other cities, this is also the case for the high-latitude city of St. Petersburg, which has long been threatened by flood events. To protect the urban area from storm surges, the Union of Soviet Socialist Republics (USSR) in 1978 approved the construction of the 25 km long Flood Prevention Facility Complex (FPFC), which was completed in 2011. The risk of flooding in the city area of St. Petersburg is amplified by the fact that large sections of the coastal area have been reclaimed from the sea. In this study, we investigate the temporal evolution of the ground displacement in St. Petersburg. To this end, we perform an extended analysis based on the application of a simplified version of the differential interferometric synthetic aperture radar technique, known as the minimum acceleration (MinA) approach. The MinA algorithm is a multi-satellite/multi-track interferometric combination technique that allows working with multiple sets of SAR images. The method allowed generation of time series of two-dimensional (2-D) (i.e. East-West and Up-Down) deformation of the terrain by processing two sequences of Sentinel-1A/B (S-1A/B) SAR images acquired from 2016 to 2018, along the ascending and descending flight passes. The Small BAseline Subset (SBAS) algorithm was independently applied to the two sets of SAR data to generate the relevant Line-Of-Sight (LOS)-projected ground deformation time series. Subsequently, the LOS-projected deformation products were geocoded and jointly combined. The results indicate that the deformation in the city is predominantly vertical (i.e. it is subsiding) with a maximum subsidence rate of about 20 mm/year corresponding to the newly sea-reclaimed lands. Finally, the error budget of the retrieved 2-D deformation time series has also been addressed.     

Accounting for image uncertainty in SAR-based flood mapping

Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with the flood maps is required.This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required.Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.     

窗口大小对SAR图像滤波效果的影响分析

滤波窗口大小的选择直接影响SAR图像滤波的效果。选择Kuan、Frost与增强Frost以及Lee与增强Lee等滤波算法,分别采用3×3、5×5、7×7、9×9及11×11(像元×像元)等大小的滤波窗口进行处理,以常规图像统计参数、平滑指数和边缘保持指数等为评价指标,对滤波效果进行定量比较。试验表明,5像元×5像元滤波窗口能够取得较好的效果。     

InSAR技术及其在地质灾害中的应用

InSAR是在太空对地球进行3维成像技术,它标志着空间遥感从2维信息获取进入到3维信息获取的新阶段,也为大地测量带来了一场革命。该技术为地质灾害的研究提供一个全新的工具。运用InSAR和D-In-SAR技术进行地面微位移监测,是近年来发展起来并得到日益重视的新方法。本文例举了InSAR技术在地震、火山喷发和滑坡等地质灾害应用的实例。表明它在形变监测研究中有广阔的应用前景,具有不可替代的优势。     

The potential of linear discriminative Laplacian eigenmaps dimensionality reduction in polarimetric SAR classification for agricultural areas

In this paper, the linear discriminative Laplacian eigenmaps (LDLE) dimensionality reduction (DR) algorithm is introduced to C-band polarimetric synthetic aperture radar (PolSAR) agricultural classification. A collection of homogenous areas of the same crop class usually presents physical parameter variation, such as the biomass and soil moisture. Furthermore, the local incidence angle also impacts a lot on the same crop category when the vegetation layer is penetrable with C-band radar. We name this phenomenon as the “observed variation of the same category” (OVSC). The most common PolSAR features, e.g., the Freeman–Durden and Cloude–Pottier decompositions, show an inadequate performance with OVSC. In our research, more than 40 coherent and incoherent PolSAR decomposition models are stacked into the high-dimensionality feature cube to describe the various physical parameters. The LDLE algorithm is then performed on the observed feature cube, with the aim of simultaneously pushing the local samples of the same category closer to each other, as well as maximizing the distance between local samples of different categories in the learnt subspace. Finally, the classification result is obtained by nearest neighbor (NN) or Wishart classification in the reduced feature space. In the simulation experiment, eight crop blocks are picked to generate a test patch from the 1991 Airborne Synthetic Aperture Radar (AIRSAR) C-band fully polarimetric data from of Flevoland test site. Locality preserving projections (LPP) and principal component analysis (PCA) are then utilized to evaluate the DR results of the proposed method. The classification results show that LDLE can distinguish the influence of the physical parameters and achieve a 99% overall accuracy, which is better than LPP (97%), PCA (88%), NN (89%), and Wishart (88%). In the real data experiment, the Chinese Hailaer nationalized farm RadarSat2 PolSAR test set is used, and the classification accuracy is around 94%, which is again better than LPP (90%), PCA (88%), NN (89%), and Wishart (85%). Both experiments suggest that the LDLE algorithm is an effective way of relieving the OVSC phenomenon.     

Remarks on correcting ionospheric distortions in L-band radar interferometry

The L-band synthetic aperture radar (SAR) interferometry (InSAR) technique has a lower accuracy due to ionospheric phase distortions. Recently, a multiple-aperture interferometry (MAI)-based ionospheric correction method has been proposed. Using four types of ionosphere-distorted interferograms, the performance of the correction method was evaluated and then analyzed the feasibility of the correction method. The test interferograms contained severe azimuth streaking, low-frequency ionospheric phase distortion and drastic phase change due to the ionosphere. The results showed that (i) the existence and magnitude of ionospheric phase distortions can be recognized from MAI interferograms and (ii) the MAI-based ionospheric correction method efficiently reduced severe azimuth streaking and low-frequency distortion but did not mitigate the drastic phase change perfectly. It is allowed (i) to determine whether a given SAR interferogram has an ionospheric distortion and (ii) to predict whether the ionospheric distortion can be corrected by using the MAI-based ionospheric correction method.     

Mexico City subsidence observed with persistent scatterer InSAR

We analyzed 23 satellite SAR (synthetic aperture radar) scenes using Persistent Scatter Interferometry (PSI) to study subsidence in Mexico City associated with groundwater withdrawal. The data were acquired by the Envisat ASAR system between January 2004 and July 2006. The spatial pattern of subsidence and the maximum subsidence rate (300 mm/year) are similar to earlier studies. Comparison to independent GPS data indicates RMS agreement between the two techniques of 6.9 mm/year, about the level expected based on joint data uncertainty. Significant annual variation in the GPS vertical data is not observed, suggesting minimal aquifer recharge during the rainy season, and justifying a simple linear model of phase variation through time for the PSI analysis.     

利用小基线集技术(SBAS)监测泉州地区地表形变

差分干涉测量(DInSAR)技术可用于监测厘米级甚至毫米级的地表形变。文中选用2007—2011年期间19景日本ALOS1/PALSAR数据,采用短基线集技术(SBAS)获取了该地区的形变时间序列和平均沉降速率。研究结果表明:泉州东南部地区整体上呈抬升态势,上升速率为5mm/a左右,主要是受到亚欧板块和西太平洋板块互相挤压作用的结果;泉州东南部有多处出现地表沉降,沉降的结果与泉州市统计的地区地下水开采数据相当吻合,因此沉降主要与地下水过度开采相关。     

DInSAR技术对不同方位形变的敏感性研究

以DEM数据为假想的地面目标,只考虑距离对相位的影响,模拟竖直向、距离向和方位向形变的干涉纹图,从而研究DInSAR技术对空间不同方位形变的敏感性。在所有模拟参数和形变位移大小都相同的情况下,三个不同方位形变所产生的相位从0到2π变化的完整干涉环的数目是各不相同的,揭示DInSAR技术对不同方位地表形变的敏感性存在差异,且敏感性由大到小依次为:竖直向、距离向和方位向。     

InSAR相位解缠算法比较及误差分析

大跨度桥梁,其变形受风力、温度、车辆等因素的影响,其变化往往达到数十厘米或者更高。这使得利用雷达干涉技术监测其变形存在相位模糊度的问题。子带干涉技术可将距离向宽带频谱分解成两个甚至多个频带,然后进行干涉处理。该技术可以使雷达波长放大10~100倍,从而为m级的变形监测成为可能。详细介绍了子带干涉技术原理和处理方法,通过分别对风力和温度影响下的青马大桥子带干涉结果进行分析,验证子带干涉技术无需进行相位解缠处理在监测大型人工建筑物形变方面的可靠性和优势。     

INSAR技术在海州露天矿边坡变形监测中的应用研究

露天矿边坡的变形监测一直是矿山安全生产的重要工作,运用合成孔径雷达干涉(INSAR)测量技术对边坡进行监测是近年来微波遥感研究的一个重要方向。本文分析了INSAR技术在边坡变形监测中的技术优势及研究的进展情况,并利用海州露天矿传统监测实测数据和INSAR监测进行比较,结果表明,INSAR技术可以实现对大型露天矿边坡有效地监测,具有一定的技术优势和广阔的应用前景。     

基于D-InSAR技术的金沙江地区滑坡形变监测与分析

滑坡是发生在我国山区的主要地质灾害类型,金沙江地区由于地势较高、地形复杂、多云多雨的特点,给传统的滑坡监测增加了难度。合成孔径雷达差分干涉测量技术(Differential interferometry synthetic aperture radar,D-InSAR)已在滑坡地面沉降监测中得到了广泛应用。本文选取金沙江上游沿岸作为研究区域,基于2018年8月11日与9月28日的Sentinel-1A影像及SRTM1数据,利用GAMMA软件及D-InSAR技术监测到金沙江地区的地表形变,成功识别出金沙江右岸的一处滑坡灾害。研究结果显示,在此滑坡的坡顶部分出现了约2.5 cm的沉降,而在坡底部分由于崩塌物的累积,地面出现了约3 cm的抬升。从实验结果可以得出,InSAR技术是一种有效的滑坡变形监测手段,利用Sentinel-1A卫星的SAR数据对滑坡区域进行形变监测,可以得到较好的干涉结果。     

利用DInSAR技术检测地裂缝分布及走向

地裂缝是一种独特的城市地质灾害,在我国分布越来越广,但人类无法阻止其活动,这严重阻碍我国经济建设的发展和危及人民的生命财产。本文利用差分干涉测量（DInSAR）技术,以河北省廊坊市大城县为例,选用2016年6月至2016年11月期间3景RADARSAT-2数据,获取了该地区的地表形变信息。参考2009年该地区的地裂缝信息,对该地的地裂缝分布及走向进行了分析及预测,为生产单位和当地政府部门对地裂缝地震灾害的治理和预防提供了决策依据。     

InSAR填海区地铁沿线地表沉降反演分析

为了更好地监测和掌握深圳填海区地铁工程结束后地铁沿线的地面沉降情况,该文利用TS-InSAR技术和20景2017年8月15日—2019年3月14日的Sentinel-1A SAR数据,借助POD精密定轨星历和ASTER GDEM V2分别去除轨道误差和地形相位,反演了深圳填海区2017—2019年地表沉降时间序列,并在此基础上重点分析了填海区地铁沿线地面沉降的时空演变规律以及地面沉降成因。结果显示,填海区各地铁沿线的地面沉降特征较为明显,最大沉降速率为-17.52 mm/a。其中,宝安中心、前海湾、深圳湾区段地铁沿线的地面沉降趋势较为严重,其地面沉降呈现逐渐增强和扩散趋势。     

基于精确距离模型的改进R-D算法

本文利用合成孔径雷达(SAR)最为精确的标准斜视距离模型,推导出了一种适合斜视SAR精确成像的改进距离-多普勒(R-D)算法。文章进一步分析了SAR的四种距离模型,将已有的三种R-D算法以及本文提出的改进R-D算法和四种距离模型之间建立了一一映射,指出了四种R-D算法之间的本质区别。最后对四种R-D算法进行了仿真,结果表明本文给出的改进R-D算法最能够适应斜视情况下的精确成像。     

SAR图像不同油膜特征的最佳分割方法选择

SAR图像溢油分割是SAR溢油监测中一个重要环节。文中选取4种不同形状、尺寸和对比度的SAR油膜数据,分别采用双峰阈值分割法、最大熵分割法、区域生长法、分水岭算法、图割法、水平集方法等6种方法进行溢油信息提取,探讨适合于不同油膜特征的最佳提取方法。结合现有尺度分割标准,提出一种SAR图像溢油信息评价指数——有效分割指数(Effective Segmentation Index,ESI),对不同分割方法得到的溢油提取结果进行定量评价,得出了不同特征油膜所适合的最佳分割方法。     

DEM数据辅助的星载SAR俯仰向数字波束形成

俯仰向数字波束形成(DBF)处理是星载合成孔径雷达(SAR)实现高分辨率宽测绘带成像的关键。然而,在处理较大地形起伏区域的回波信号时,传统俯仰向波束扫描(SCORE)方法会出现波束指向偏差问题,导致接收回波的增益降低,影响SAR系统成像性能。针对这一问题,本文详细分析了高分宽幅星载SAR系统的俯仰向DBF接收波束扫描指向问题,提出了一种基于数字高程图(DEM)的俯仰向DBF处理方法。该方法基于星载SAR成像几何模型,首先利用成像场景的DEM数据和卫星轨道参数计算距离门单元对应的地面高程值,并进一步计算距离门单元对应的目标波达角,然后根据该对应关系计算每个距离门单元的俯仰向DBF加权矢量,从而确保在俯仰向DBF处理过程中接收波束指向正确。通过X波段的星载SAR系统进行仿真实验,结果表明(1)当地面高程值大于1.9 km时,传统SCORE方法处理得到的目标信号幅度下降都超过2.8 dB,本文方法处理得到的目标信号幅度下降都小于0.4 dB,本文方法优于传统SCORE方法;(2)由DEM数据误差导致的地面目标高程偏差对本文方法影响较小。因此本文方法能够有效改善地形起伏较大区域的回波信号接收增益。     

有偏迭代估计在短基线集形变模型中的应用

进行SBAS技术时,选择的干涉对一般会出现一景影像和其他几景影像干涉的现象,导致构建的线性形变模型病态。在这种情况下,最小二乘法求解SBAS线性形变模型已不太适合。针对这一问题,本文结合Liu估计的有偏迭代法在求解病态线性模型时能够克服病态性对结果的影响,提出将此方法应用到求解SBAS线性形变模型中。以覆盖济宁地区的13景ENVISAT ASAR数据展开了SBAS技术在城市地面沉降监测中的应用实验,分别利用LS法、基于Liu估计的有偏迭代法解算形变模型。结果表明,基于Liu估计的有偏迭代法获得研究区的年平均沉降速率标准差比LS法获得的低1.4,且其求解的结果相较于LS法获得的结果更准确、稳定。