INTERFEROMETRIC SAR DEM CONSTRUCTION FOR LANDSCAPE PROCESS ANALYSES IN NORTH-EASTERN ICELAN

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“空-天-地”一体化技术在滑坡隐患早期识别中的应用——以兰州普兰太公司滑坡为例

“空-天-地”一体化技术在滑坡隐患早期识别中的应用——以兰州普兰太公司滑坡为例

第31卷第6期2020年12月中国地质灾害与防治学报TheChineseJouenaoooGeooogicaoHaaaed and Conteoo Voo.31 No.6Dec.2020D0N10. 16031/j. cnki. issn. 1003-8035.2020. 06. 02空•天•地"一体化技术在滑坡隐患早期识别中的应用一*以兰州普兰太公司滑坡为例侯燕军,周小龙,石鹏卿,郭富赞(甘肃省地质环境监测院,甘肃 兰州 730050)摘要:长时间序列SBAS-nSAR 形变监测,能够减弱误差带来的影响,提高监测精度,有效识别地质灾害隐患#研究获取了兰州地区2019年9月至2020年4月的L 波段升轨ALOS-2编程数据,利用“空-天-地”一体化地质灾害监测体系,基于小基线集(SBAS-nSAR )技术对兰州市普兰太有限公司滑坡进行了有效识别#经现场核查,滑坡宏观变形迹象明显,并与同期C 波段Sentinei1A 升轨数据处理对比分析,表明基于L 波段的SBAS-nSAR 形变监测在兰州市典型滑坡早期识别中发挥了很好的作用,可以在区域滑坡早期识别中推广应用# 关键词:“空-天-地”一体化;早期识别;地质灾害;SBAS-InSAR中图分类号:P642. 22文献标识码:A文章编号#1003-8035 (2020) 06-0012-09Application of “ Air-Spacc-Ground & integrated technology in early idenhncahon of landslide hidden danger : taking LanzZou PulantaiCompany Landsline as an exampleHOU Yanjun , ZHOU Xiaolong , SHI Pengqing , GUO Fuyun(Gansu Institute of Geological Environment Monitoring , Lanzhou , Gansu 730050 , China )Abstract : The deformation monitoring of SBAS-nSAR with long time series can reduca the influenca of errors ,impeoeethe monito eing accueacy " and e o ectieeoy identioy the hidden dange e o ogeo oogica odisaste es. The peogeammingdataooL-band eoeeated oebitALOS-2 in themain ueban aeeaooLanahou oeom Septembee2019 toApeio2020 weeeobtained in this study. The oands oide o oLan ahou PuoantaiCo. " Ltd. was e o ecti ee oy identi oiedbased on the smal l baseline set ( SBAS-nSAR ) technology by using the intecrated geelogical disastermonito eing system oo % integeation ooAie-Space-Geound & .Theough on-site ee ei oication " the maceoscopic deooemation signs o othe oands oide a ee ob eious " and thecompaeison and ana oysis with thesentineo-1 A oebit eising data p eoce s ing o othe C-band du eing the same pe eiod show thattheDeooemation monito eing o oSBAS- InSAR based on the L-band plays a veg good role in the eagy identification of typical landslides in LanzhouCity " and can bepopuoaeiaed and appoied in the ea eoy identi oication ooeegionaooandsoides.Keywords : inteogtion of “ AimSpaca 天r ound ” ; esrly identification ; geelogical disaster ; SBAS-nSAR收稿日期:2020-08-23 '修订日期:2020-09-24基金项目:甘肃省科技重大专项-社会发展类(19ZD2FA002)第一作者:侯燕军(1979-),甘肃秦安人,高级工程师,硕士研究生,主要从事水文地质、地质灾害、地质环境评价、地质灾害信息化建设等方面的研究 # E-mail :tighyj@ 163. ccm通讯作者:周小龙(1995-),男,甘肃漳县人,遥感科学与技术专业,本科,助理工程师,主要从事遥感技术在地质灾害防治方面的应用研究 o E-mail :zhoulongwiser@ 163. com第6期中国地质灾害与防治学报-13-0引言黄土高原地区是以黄土为主体的区域地貌,在以新构造活动为主的内动力和流水为主的外动力作用下形成的黄土高原地貌,为地质灾害的形成提供了空间条件,再加上特殊的气候条件、植被属性和土体特征,决定了地质灾害在区域空间上的分布[1],因此黄土高原地区极易发生地质灾害,严重威胁到人民群众生命财产安全#兰州地处黄土高原西部,属于黄河上游地区,青藏高原隆起区的东北边缘,青藏高原和黄土高原的交会处,市区属特殊的“两山夹一谷”的河谷地貌-2]。

永久散射体雷达干涉技术

永久散射体雷达干涉技术

• 计算P在从影像中的像素坐标Ps(rs,cs),Ps和Pm就是 一对同名点。
• 粗配准:从两幅SAR影像中识别出少量同名 点,基于其像素坐标偏移量,通过简单平 移,使主从影像同名点基本对应于同一分 辨单元。 • 精配准:精确搜索同名点的准确位置,通 过对从影像进行坐标变换和像元值插值重 采样,实现同名像点精确对应于地面同一 分辨单元。 • 相干系数法、最大频谱法、相位差影像平 均波动函数法
D
振幅信息双阈值法
• 1.振幅阈值法探测PSC(强反射性) • 计算所有像素的时序振幅值,获得振幅阈值:
• 分析各像素在辐射定标且配准后的SAR影像上的振幅时间 2, N T 序列值Ak,如果 minA k 1, 确定为 PSC
k A
m n 1 TA min A k i,j k 1 , 2, ,N m n i 1 j1
The
end
InSAR简介
• 是什么? • InSAR是利用不同空间位置获取同一目标的至少两幅SAR 影像,开展干涉测量数据处理,通过同一目标两次回波信 号的相位差获取该目标的高程或形变信息。 • 为啥有? • 1.早期SAR影像只利用灰度信息,忽视相位信息; • 2.受杨氏双缝干涉实验启发。 • 怎么用?
基本原理
2~4幅影像即可
一般不作平滑处理 突然、形变量大的地表活 动
20~30幅SAR影像
PSInSAR存在的主要问题
• • • • 1.自然地表PS密度低 角反射器成本高,不适合大规模推广 2.大气相位估计精度低 大气相位受水汽、温度、地形等复杂因素 影响,并与基线误差相位、DEM误差相位 耦合,目前没有哪一种方法具有很高的普 适性。 • 3.形变检测性能分析理论尚不成熟。

合成孔径雷达差分干涉测量

合成孔径雷达差分干涉测量
基于三种假设:
1. 只有形变对干涉图收到形变的影响; 2. 形变对于干涉图中形变不会影响有地面高程产生
的相位发生跳跃; 3. 地形对干涉图可以获得精确的DEM。
差分干涉测量的原理
四轨法
基本思想是选择四幅SAR图像,用其中 的两幅来生成DEM,另外两幅作变形监测。
三种方法比较
两轨法
优点:不需要相位解缠,减少了数据处理 的工作 量;避免了相位解缠引入的误差。
来消除地形相位。 在两轨法中,外部DEM的精度、空
间分辨率、插值方法及干涉基线对形 变量的精度都有显著的影响。
差分干涉测量的原理
三轨法
是由1994年由Zebker等人提出的,由 于该方法可以直接从SAR图像中提取出地 表形变信息,被认为是差分干涉模型最经 典的方法。
差分干涉测量的原理
三轨法
原理是采用三幅SAR图像,以其中的一幅作 为主图像,另外两幅作为从图像,可与主图像分 别生成两幅干涉图。
差分干涉测量在地震监测的应用
差分干涉测量地震监测的应用
差分干涉测量地震监测的应用
地震可以引起电离层异常
差分干涉测量在地表沉降监测的应用
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合成孔径雷 达差分干涉
测量
引言
合成孔径雷达(Synthetic Aperture Radar,简称SAR),是一
种工作在微波波段的主动式微波成像传感器。它有效地解 决了雷达设计中高分辨率要求与大天线、短波长之间的矛 盾,使分辨率提高了数百倍。
合成孔径雷达干涉测量(Interferometric Synthetic Aperture
缺点:已知DEM与InSAR干涉图像的配准存 在很大 困难。

DInSAR技术资料整理

DInSAR技术资料整理

DInSAR全称Differential Interferometric Synthetic Aperture Radar,合成孔径雷达差分干涉测量技术。

➢InSAR技术提取地表DEM,需要假设两次成像期间,地表没有发生变化,地物产生的随机相位也是不变的。

➢而DInSAR则是一种根据多期SAR数据,获取地表形变信息的方法之一。

根据差分干涉所需影像的多少,DInSAR可以分为:二轨法,三轨法和四轨法。

⏹二轨法:利用两景影像,主影像为形变后获取的数据,辅影像为地表形变前获取的数据。

将两者进行干涉处理,生成干涉图,干涉图中包括地形相位和形变相位,然后引入外部DEM数据,将DEM数据模拟成地形相位,从干涉图中减去,即可得到地表的形变相位。

优点:所需SAR数据少缺点:外部引入的DEM包含的误差会影响最终的差分干涉结果流程图:⏹三轨法:利用三景影像,其中两景是形变发生前获取的数据,另一景是形变后获得的,选区形变前后两景影像中的一景为主影像,其余为辅影像,分别和主影像进行配准,这样便生成两组干涉相位,一组是形变前的,只有地形信息;一组是形变后的,包含形变信息和地形信息;然后将形变后干涉相位减去已经解缠的形变前的相位,得到只含有形变信息的干涉相位,最后进行相位解缠,相位转高程和地理编码,获取地表的形变信息。

优点:无需外部DEM数据及其引入的DEM误差;缺点:需要进行相位解缠,解缠结果的好坏直接影响差分的结果;流程图:四轨法:与二轨法类似,但是不需要外部引入的DEM数据,需要四景影像;基本思路是将形变发生前获取的两幅影像进行干涉处理,得到形变前的干涉相位,只包含地形信息;然后将形变后的两景影像进行干涉处理,得到形变后的干涉相位。

从形变后的干涉相位中减去形变前的干涉相位,得到地表的形变相位,然后相位解缠,得到差分干涉图。

流程图:InSAR获取DEM条件:两期影像获取期间地物没有明显的形变,且地物产生的随机相位是相同的;流程图:。

dem在insar中的作用

dem在insar中的作用

dem在insar中的作用
DEM(Digital Elevation Model,数字高程模型)在InSAR (Interferometric Synthetic Aperture Radar,合成孔径雷达干涉测量)中起着关键作用。

DEM是地球表面的三维高程模型,用于表示地面的海拔高度。

DEM在InSAR中的作用包括以下几个方面:
1. 坡度计算:基于DEM,可以计算每个点的坡度,从而帮助理解地表形态的变化。

2. 位移测量:使用InSAR进行干涉测量时,需要使用DEM来排除地球曲率对测量结果的影响,从而更精确地计算目标物体的位移。

3. 地形校正:DEM可以用于校正InSAR图像中的不同地形效应,如斜坡、山谷等,以获得更准确的地表变形信息。

4. 反演应力:借助DEM,可以获取地表的三维几何形态,结合InSAR的测量结果,可以反演地壳中的应力分布,进一步研究地壳运动和地震活动。

5. 地理信息系统(GIS)应用:DEM可以与其他空间数据集相结合,用于地理信息系统分析和应用,如地貌分析、水资源管理、土壤侵蚀研究等。

综上所述,DEM在InSAR中发挥了关键作用,为提供精确的地表变形信息和地球表面特征提供了重要数据基础。

多发多收干涉合成孔径雷达高程测量关键技术研究

多发多收干涉合成孔径雷达高程测量关键技术研究

多发多收干涉合成孔径雷达高程测量关键技术研究多发多收干涉合成孔径雷达高程测量关键技术研究摘要:合成孔径雷达(Synthetic Aperture Radar, SAR)是一种通过合成大尺寸的孔径实现高分辨率成像的雷达系统。

随着空间技术的发展和对高精度地面测绘需求的增加,干涉合成孔径雷达(Interferometric Synthetic Aperture Radar, InSAR)成为一种重要的高程测量技术。

本文结合多发多收干涉合成孔径雷达(Multi-Baseline InSAR, MB-InSAR)技术,探讨了干涉测量的原理、关键技术以及在高程测量中的应用。

一、引言合成孔径雷达技术作为一种无依托地面控制点的高程测量方法,在地质灾害监测、数字地形模型生成等领域具有广阔的应用前景。

干涉合成孔径雷达技术,通过多次雷达观测,利用相干性检测原理计算相位差,从而实现高程信息的提取。

本文重点研究多发多收干涉合成孔径雷达技术在高程测量中的关键技术。

二、多发多收干涉合成孔径雷达原理多发多收干涉合成孔径雷达是指通过多颗雷达卫星的多角度观测,利用双差技术消除大气影响,获取高精度的相位差信息。

首先,将两次观测的干涉相位差计算成干涉图像;然后,利用干涉图像进行相位解缠,得到相位差;最后,通过相位差转换为高程信息。

三、多发多收干涉合成孔径雷达关键技术1. 预处理技术:预处理是多发多收干涉合成孔径雷达的关键步骤,包括去除噪声、平滑滤波、相干性检测等。

预处理的质量直接影响后续高程测量的精度和可靠性。

2. 干涉图像配准:干涉图像配准是多发多收干涉合成孔径雷达的核心技术之一。

多源雷达数据的配准对于提高干涉相位差计算的精度至关重要。

3. 相位解缠技术:相位解缠是多发多收干涉合成孔径雷达中的难点之一。

相位解缠技术包括利用全局模型、局部模型以及非正则化方法解决相位闭合问题。

4. 大气校正技术:大气因素是多发多收干涉合成孔径雷达高程测量中的重要误差来源。

基于“天空地”一体化技术的安全监测系统建设与应用

基于“天空地”一体化技术的安全监测系统建设与应用

第 47 卷第 3 期2021 年 3 月水力发电基于$天空地%—体化技术的 安全监测系统建设与应用邬昱昆1,2 ,肖亚子1 2 ,樊恒通1 2 ,郑 浩1 '2 ,李江浪1 2(1.水能资源利用关键技术湖南省重点实验室,湖南长沙410014 ;2.中国电建集团中南勘测设计研究院有限公司,湖南长沙410014)摘 要:西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡一堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响#为保障下游水电站建设安全,对拉 哇库区主要不良地质体建立了基于星载InSAR 技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果# 关键词:“天空地”一体化技术;安全监测系统;不良地质体;拉哇库区Construction and Application of Safety Monitoring System Based on Spacc-Air-Ground Integrated TechnologyWU Yukun 1'2, XIAO Yazi 1'2, FAN Hengtong 1'2, ZHENG Hao 1'2, LI Jianglang 1'2(1.Hunan Provincial Key Laboratoro of Hydropower Development Key Technoloay, Changsha 410014, Hunan, China;2. PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, Hunan, China)Abstract : On October 11 / 2018 and Novembee 3 / 2018, teo larae-sca1e landslides on the right bank of the Jinsha River inBaige Villaae, Jiangda County, Tibet block the rivee to form barrier lake, and the food of weir failure infuences the stabilita of the poor geeloaical bodies in d ownstream Raws Reserveir area to varying decree. In order to ensure the sate ccnstruction ofdownstream hydropower station, a space-aircround integrated monitoring and early warning system based on space-borne InSARtechnolocy, UAV technolocy and ground sensorr is established to monitor the poor geeloaicat bodies in Raws Reserveir area andobtain deformation information with multi-dimensional space acquisition technolocy. The deformation information is processed, analyeed and eiualieed in aoeal-timemanneothoou/h theinte l i/entmonitooin/plateoom,and the/oaded alaomGaoeineoomed tooeleeantpeoGonnelbyplatooom and SMS.Key Wordt Gpace-aio-/oound inte/oated technolo/y; Ga oety monito oin/ GyGtem; poo o/eolo/ical body; Rawa oeGe oeoi oa oea中图分类号:TV698文献标识码:A文章编号:0559-9342 (2021) 03-0054-040引言金沙江上游由于地震活动频繁,河谷强烈下切, 岸坡高陡狭窄,岩体破碎,历史上发生了较多大规 模的滑坡堵江事件⑴# 2018年10月11日和2018年 11月3日,西藏自治区江达县波罗乡白格村附近金沙江右岸先后发生2次大规模高位滑坡堵江事件, 堰塞湖库容最大超过5亿m 3 # 11月13日溃堰时, 溃口洪峰流量达31 000 m 3/s ,至下游拉哇水电站流 量约22 000 m 3/s ,虽然洪峰持续时间短,但洪峰流量达坝址万年一遇洪水的2倍。

基于卫星实景三维和InSAR技术的地质灾

基于卫星实景三维和InSAR技术的地质灾

基于卫星实景三维和InSAR技术的地质灾害隐患识别监测文|涂宽 郑健 马卫胜 文强二十一世纪空间技术应用股份有限公司摘要:我国地质灾害频发,地质灾害隐患识别对提高地质灾害防治业务能力、保护人民的生命财产安全具有重要意义。

光学卫星遥感图像可判读性强、信息量丰富,被广泛运用在地质灾害隐患早期识别工作中。

甚高—高分辨率卫星立体三维产品能为地质灾害易发区的三维重建提供数据基础,实现从二维图像到三维数据体的提升,大大提高卫星遥感地质灾害隐患识别和监测能力。

本文结合北京系列卫星甚高—高分辨率卫星实景三维产品和星载合成孔径雷达差分干涉测量技术(InSAR),论述综合遥感技术在地质灾害调查和监测中的应用,通过开展地质灾害隐患早期识别,为地方地质灾害防治及突发地质灾害应急响应工作提供参考。

一、前言地质灾害是由于地质运动或人类工程活动,引发危害人类生命财产安全、破坏自然资源环境的现象或过程。

常见的地质灾害包括滑坡、崩塌、泥石流、塌陷、地裂缝等。

中国幅员辽阔,地形地貌和地质构造复杂,崩塌、滑坡、泥石流等地质灾害具有高隐蔽性、强突发性的特点。

开展地质灾害隐患的早期识别工作,对地质灾害监测预警、灾害风险区把控、防灾减灾工作有着重要的指导意义。

2020年我国开展了首轮“国家级”大规模地灾隐患综合遥感识别工作,经过充分论证与应用实践,确立了综合应用空天地多源遥感观测技术,以“形态、形变、形势”为识别内容的重大隐蔽性地灾隐患早期识别技术思路,形成了包括专题信息提取、隐患特征识别、野外核查验证的业务流程,丰富了地灾调查评价业务工作方式[1]。

高分辨率光学遥感影像具有要素清晰、形象直观等特点,广泛应用于地质灾害隐患识别领域。

基于多源多时相的甚高—高分辨率光学遥感影像,动态监测分析地面目标的光谱、纹理等信息差异,识别地质灾害隐患。

合成孔径雷达干涉测量技术(interferometric synthetic aperture radar, InSAR)[3]利用合成孔径雷达卫星(synthetic aperture radar, SAR)在同一地区获取重复轨道上两景或多景影像进行干涉处理获取地表形变信息。

基于角反射器的越江大桥InSAR_形变监测方法研究

基于角反射器的越江大桥InSAR_形变监测方法研究

第11期2023年4月江苏科技信息JiangsuScienceandTechnologyInformationNo 11April,2023作者简介:吴铭飞(1988 ),男,江苏江阴人,工程师,博士;研究方向:桥梁变形监测技术㊂基于角反射器的越江大桥InSAR形变监测方法研究吴铭飞(上海城建城市运营(集团)有限公司,上海200023)摘要:星载InSAR技术具有获取地表大范围㊁高精度形变位移信息的能力,已经成为对地形变观测的有效技术手段之一㊂文章将C波段SAR影像用于越江大桥形变监测,利用角反射器提高监测结果可靠性与精度㊂以上海长江大桥作为监测对象,在大桥主桥和邻近区域安装4台角反射器,采用大桥区域时间跨度2020年9月至2022年3月的Sentinel-1A卫星影像,通过多时相InSAR分析技术,获得了角反射器所在位置大桥形变速率与时序变化情况㊂研究结果表明,本文提出的基于角反射器的越江大桥InSAR形变监测方法可以实现越江大桥高精度形变监测㊂关键词:InSAR;角反射器;越江桥梁;形变监测中图分类号:U446 2㊀㊀文献标志码:A0㊀引言㊀㊀合成孔径雷达(SyntheticApertureRadar,SAR)是自20世纪50年代开始发展的一种微波成像遥感技术㊂微波遥感可以穿透云雨,不受昼夜和气候的影响,能够实现全天时㊁全天候观测成像,甚至能够穿透植被和地表获取信息㊂另外,合成孔径技术极大改善了雷达成像分辨率,星载SAR卫星被广泛应用于远距离㊁大范围的对地监测中,尤其在灾害监测㊁环境监测㊁海洋监测㊁资源勘查㊁农作物估产㊁测绘和军事等方面具有独特的优势㊂合成孔径雷达干涉测量(InterferometricSyntheticApertureRadar,InSAR)技术在近30年内发展迅速,尤其是时序InSAR技术的提出,通过对永久散射体(PermanentScatterer,PS)的干涉相位时序分析,获取高密度㊁高精度的地表沉降信息,使得InSAR成为地表形变监测的主要技术手段之一[1-2]㊂InSAR形变计算的精度与可靠性很大程度上取决于PS点的相位相干性和信号稳定性,可以利用散射信号稳定㊁相干性高的角反射器(CornerReflector,CR)来提高PS点密度与InSAR形变监测计算精度㊂本文将对现有InSAR变形监测技术特点进行阐述与分析,以上海长江大桥为研究对象,利用越江大桥及周边区域布设的角反射器作为辅助手段,基于星载InSAR技术监测上海长江大桥结构变形,并对角反射器散射效果和越江大桥形变特征进行分析㊂1㊀InSAR变形监测技术原理㊀㊀InSAR技术基于时间测距成像机理,通过卫星上装载的两副SAR天线同时观测(单轨双天线模式),或两次平行的观测(重复轨道模式),获得同一区域的重复观测数据,即单视复数影像对㊂由于两副天线和观测目标之间的几何关系发生变化,同一目标对应的两个回波信号之间产生相位差,由此得到的相位差影像通常称为干涉图,结合观测平台的轨道参数和传感器参数等可以获得地面高程信息[3]㊂在此基础上,若需进一步获得地面目标几何位置相对于SAR传感器发生的变化(即形变),则需要去除干涉相位中平地㊁地形等因素对相位的影响,这个过程被称之为差分干涉测量(DInSAR)㊂根据地形相位去除方法的不同,DInSAR可以分为二轨法㊁三轨法和四轨法,其中以二轨法最为常见㊂近年来,越来越多的高分辨率SAR卫星发射并投入使用,InSAR监测领域由宏观㊁大尺度的区域地表监测拓展至更微观㊁局部的城市基础设施监测㊂交通基础设施是人居环境的重要组成部分,其结构健康问题关乎市民出行安全㊂多时相InSAR(Multi-TemporalInSAR,MT-InSAR)的出现与发展进一步提高了基础设施监测的精准化与精细化㊂时至今日,InSAR已经成为道路设施全天时㊁全天候㊁大范围㊁高精度变形监测的有效技术手段㊂2㊀基于角反射器的InSAR数据处理流程2 1㊀角反射器设计原理㊀㊀角反射器是SAR定标中使用较为广泛的无源点目标,一般具有大且稳定的雷达散射截面积(RadarCrossSection,RCS),其RCS远大于周围环境的散射,并且表现出与雷达波长和角反射器尺寸无关的3dB波束带宽(见图1)㊂角反射器一般采用铝制金属面板,结构简单㊁性能稳定㊁架设容易㊁成本低廉,固定安装于待监测区域㊂由于角反射器的散射特征和空间位置稳定,不仅可以作为SAR辐射标定参考目标,还可以作为几何参照物,用于几何定标和InSAR形变参考㊂图1㊀角反射器工作原理目前,使用的角反射器大多采用三条棱边等长的三面角结构形式㊂常见的角反射器有矩形三面角反射器㊁扇形三面角反射器和三角形三面角反射器,其性能参数如表1所示[4]㊂表1㊀三类角反射器性能参数类型RCS最大值3dB带宽/(ʎ)平均RCS矩形三面角反射器12πb4/λ2250 7b4/λ2扇形三面角反射器15 6b4/λ2320 47b4/λ2三角形三面角反射器b4/3λ2400 17b4/λ2㊀注:b分别为矩形角反射器的正方形边长㊁扇形角反射的扇形半径和三角形角反射器的直角边长;λ为工作波长㊂三角形三面角反射器的3dB带宽大于矩形和扇形三面角反射器,但其RCS值小于另外两种角反射器(见表1)㊂相关研究表明,当入射角度变化时,三角形三面角反射器的RCS值缩减速率最小,在较大的角度范围内可以获得较大的回波功率㊂在实际定标过程中,角反射器朝向不可避免偏离SAR雷达波入射方向,必须保证角反射器在较宽入射角度范围内都能取得较大的RCS㊂因此,三角形三面角反射器的使用最为广泛㊂本文亦选取三角形三面角反射器作为形变参考点进行形变监测解算㊂2 2㊀InSAR数据处理方法㊀㊀干涉相位是InSAR处理分析的基础㊂在理想情况下,两幅SAR影像的干涉相位只与参考面㊁地形及地表形变有关㊂但在实际观测过程中,两次观测期间的目标散射特性㊁观测视角㊁大气条件等都有可能发生变化,干涉相位受失相干㊁大气延迟㊁地形相位补偿误差㊁卫星定位误差㊁相位解缠误差等因素综合影响㊂为了消除上述误差对真实形变相位解算的影响,产生了以PS和SBAS技术为代表的MT-InSAR时序分析技术[5-6]㊂MT-InSAR技术对构成干涉相位的各相位分量进行建模,真实的干涉相位组成如下:φ=φflat+φtopo+φdefo+φorb+φatm+φnoise式中:φ为干涉相位;φflat为平地相位;φtopo为地形相位;φdefo为形变相位;φorb为轨道误差相位;φatm为大气影响相位;φnoise为噪声相位㊂基于差分相位信息建立相位函数模型,将φtopo地形相位㊁φorb轨道相位以及φatm大气延迟相位从干涉相位中分离出来,得到φdefo形变相位,进而计算出地面各点的形变信息,其处理过程如图2所示㊂图2㊀MT-InSAR时序分析处理流程3㊀角反射器布设方式3 1㊀上海长江大桥简介㊀㊀上海长江大桥位于中国上海市,东起上海市崇明岛,上跨长江水道,北至长兴岛与陈海公路相接后,汇入向化公路跨线桥㊂大桥于2004年12月28日动工兴建,于2009年10月31日通车运营㊂大桥总面积34 23万平方米,线路长16 63千米,跨越长江部分正桥长9 97千米;桥面为双向六车道高速公路,设计速度100千米每小时㊂大桥选择了独特的 人 字形结构斜拉桥造型,相应于桥塔构型,主梁采用了分离结构,是上海市地标性建筑㊂大桥所处位置与实景照片,如图3所示㊂3 2㊀角反射器的安装㊀㊀为提高上海长江大桥InSAR形变监测精度,项目组在上海长江大桥及附近区域安装了4个三角形角反射器,角反射器直角边长为1 2米㊂其中,一个布设于上海市长兴岛隧桥管控中心,编号CRCX,如图4a所示;另外,3个角反射器布设在长江大桥上,编号图3㊀上海长江大桥位置与实景为CR1,CR2和CR3,3个角反射器的现场安装情况分别如图4b,4c和4d所示㊂图4㊀角反射器安装现场考虑到野外防风和防积水,角反射器上安装了电磁波可穿透的聚乙烯塑料材质盖板㊂此外,大桥上安装的角反射器设计了专门的固定支架,可在不损害大桥表面结构的情况下,将角反射器平稳地固定在桥梁上下行车道中间的隔离带和叠合梁上㊂为了达到对SAR卫星发射微波脉冲最佳的反射效果,角反射器安装的朝向垂直于卫星航向,并通过调整倾角使得角反射器的中心指向线对准雷达微波的入射方向㊂4㊀基于MT-InSAR的上海长江大桥形变监测4 1㊀影像数据㊀㊀为了充分利用上海长江大桥及周边区域安装的角反射器,本文选用2020年9月 2022年3月覆盖上海长江大桥的44景Sentinel-1A卫星平台升轨SLC单视复影像为数据源,观测模式为IW宽幅干涉,分辨率为5米ˑ20米,极化方式为VV极化㊂选取2021年6月30日的影像为PS处理主影像㊂本文采用由欧空局开发的SAR影像处理软件(SeNtinelApplicationPlatform,SNAP)进行影像数据处理,基于USGS发布的30米分辨率SRTMDEM数据去除地形相位并进行地理编码,完成轨道校正㊁条带选择㊁主影像选取㊁配准与干涉图生成等处理步骤㊂辐射定标前后角反射器所在区域影像如图5所示㊂在强度影像中,角反射器区域表现为非常明亮的十字光斑,所在区域信噪比有极大的提升,可以作为稳定的干涉测量形变参考点㊂本文采用StaMPS进行时序分析与形变提取㊂StaMPS/MTI(StanfordMethodforPersistentScatterers/Multi-TemporalInSAR)方法由英国利兹大学Hopper教授等学者于2004年提出,该方法采用三维时空解图5㊀角反射器安装前后强度对比缠算法获取目标的时序形变信息,同时支持PS与SBAS处理方法,能提高时序InSAR在低相干区的监测能力㊂基于该方法,本文得到区域形变速率解算结果如图6所示㊂图6㊀区域形变速率4 2㊀形变监测结果分析㊀㊀在默认情况下,StaMPS方法以解算得到的区域内所有PS点的 相位 形变 量平均值作为相对值,计算各点的相对形变量㊂为了更准确地获取越江大桥重点位置真实形变量,本文将长兴岛角反射器作为形变参考点,计算角反射器所在3处桥梁位置在2020年9月至2022年3月间的绝对形变量变化情况,结果如图7所示㊂由形变时序曲线图可见,CR1,CR2和CR3在监测期间内形变波动较小,形变区间基本处于以形变量0为对称轴ʃ10mm范围内,符合正常运行状态下越江大桥形变变化特征㊂其中,CR1,CR2的形变波动范围比CR3更小,其主要原因是CR3安装于主桥斜拉桥段,相比于非斜拉桥段,斜拉桥形变状况更容易受温度㊁荷载变化影响㊂因此,利用角反射器可以实现对越江大桥形变的有效㊁高精度监测㊂监测结果表明,上海长江大桥主桥结构稳定,未产生明显的沉降趋势㊂图7㊀角反射器位置示意及形变曲线5 结论㊀㊀本文基于欧空局Sentinel-1A卫星平台2020年9月至2022年3月共44景SAR影像对越江大桥变形监测方法开展研究㊂以上海长江大桥为研究对象,在大桥主桥和周边区域安装角反射器,采用MT-InSAR时序分析技术,得到角反射器位置大桥形变监测结果,上海长江大桥结构稳定,无明显沉降位移趋势㊂研究结果表明,角反射器可以极大地增加监测位置的雷达反射信号强度,有助于提高越江大桥InSAR变形监测成果的精度和可靠性㊂本文提出的基于角反射器的越江大桥InSAR变形监测方法对于运营期特大型桥梁结构健康监测与安全风险管控相关工作具有借鉴意义㊂参考文献[1]何秀凤,高壮,肖儒雅,等.InSAR与北斗/GNSS综㊀㊀合方法监测地表形变研究现状与展望[J].测绘学报,2022(7):1338-1355.[2]李振洪,朱武,余琛,等.雷达影像地表形变干涉测量的机遇,挑战与展望[J].测绘学报,2022(7):1485-1519.[3]朱茂,沈体雁,黄松,等.基于COSMO-SkyMed数据的水库边坡InSAR形变监测应用[J].水力发电学报,2018(12):11-21.[4]张婷,张鹏飞,曾琪明.SAR定标中角反射器的研究[J].遥感信息,2010(3):38-42.[5]路聚峰.时间序列高分辨率COSMO-SkyMed影像地表形变监测研究[D].阜新:辽宁工程技术大学,2017.[6]潘超,江利明,孙奇石,等.基于Sentinel-1雷达影像的成都市地面沉降InSAR监测分析[J].大地测量与地球动力学,2020(2):198-203.(编辑㊀何琳)InSARdeformationmonitoringmethodofcross-riverbridgebasedoncornerreflectorWuMingfeiShanghaiUrbanOperationGroup Co. Ltd. Shanghai200023 ChinaAbstract SpaceborneInSARtechnologyhastheabilitytoacquirelarge-scaleandhigh-precisionsurfacedeformationanddisplacementinformation andhasbecomeoneoftheeffectivetechnicalmeansforterraindeformationobservation.Inthispaper C-bandSARimagesareusedtomonitorthedeformationofthecross-riverbridge andcornerreflectorsareusedtoimprovethereliabilityandaccuracyofthemonitoringresults.ShanghaiYangtzeRiverBridgeisusedasthemonitoringobject andfourcornerreflectorsareinstalledonthemainbridgeandaroundtheadjacentareaofthebridge.UsingtheSentinel-1AsatelliteimagesofthebridgeareawithtimespanfromSeptember2020toMarch2022 thedeformationrateandtimingchangesofthebridgeatthepositionofthecornerreflectorareobtainedbasedonthemulti-temporalInSARanalysistechnology.TheresearchresultshowsthattheInSARdeformationmonitoringmethodofthecross-riverbridgebasedonthecornerreflectorproposedinthispapercanrealizehigh-precisiondeformationmonitoringofthecross-riverbridge.Keywords InSAR cornerreflector cross-riverbridge deformationmonitoring。

浅谈SAOCOM雷达在广西北部山区地质灾害识别中的应用

浅谈SAOCOM雷达在广西北部山区地质灾害识别中的应用

世界有色金属 2023年 7月上46测绘技术M apping technology浅谈SAOCOM 雷达在广西北部山区地质灾害识别中的应用赖嘉宁,何卫军,梁明月,杨 倩(广西壮族自治区遥感中心,广西 南宁 530023)摘 要:尽管近年来国家大力开展地质灾害详查、群防群测等工作,且取得一定成效,但面对地形复杂、植被覆盖度高的广西北部山区,规模小、影响范围大的地质灾害隐患的识别难度依旧不减。

本文以广西北部山区九万大山南麓作为研究区,基于SBAS-InSAR技术,采用L波段的SAOCOM雷达数据,结合地质相关专业知识,综合讨论SAOCOM数据在该地区地质灾害隐患识别的应用,为提升同类地区地质灾害隐患识别提供一定参考。

关键词:SBAS-InSAR;SAOCOM;地质灾害隐患识别中图分类号:P694 文献标识码:A 文章编号:1002-5065(2023)13-0046-3Discussion on the Application of SAOCOM Radar in Geological Disaster Identificationin the Northern Mountainous Areas of GuangxiLAI Jia-ning, HE Wei-jun, LIANG Ming-yue, YANG Qian(Remote Sensing Center of Guangxi Zhuang Autonomous Region,Nanning 530023,China)Abstract: Although the country has vigorously carried out detailed geological hazard surveys and mass prevention surveys in recent years, and has achieved certain results, the difficulty of identifying geological hazard hazards with small scale and large impact areas in the mountainous areas of northern Guangxi, which have complex terrain and high vegetation coverage, remains unabated. This article takes the southern foot of 90000 Dashan Mountain in the northern mountainous area of Guangxi as the research area. Based on SBAS-InSAR technology, L-band SAOCOM radar data is used, combined with geological related professional knowledge, to comprehensively discuss the application of SAOCOM data in the identification of geological hazards in this area, providing a certain reference for improving the identification of geological hazards in similar regions.Keywords: SBAS InSAR; SAOCOM; Identification of geological hazard hazards收稿日期:2023-04课题项目:基于“天-空-地”一体化的广西滑坡灾害遥感早期识别与监测研究(桂地矿综研〔2022〕10号)。

基于时序InSAR技术的铜川北矿区地表形变监测

基于时序InSAR技术的铜川北矿区地表形变监测

基于时序InSAR技术的铜川北矿区地表形变监测发布时间:2021-12-21T08:31:10.149Z 来源:《工程建设标准化》2021年第20期作者:亢邈迒[导读] 煤矿采空区作为铁路选线的重要控制性因素,在线路设计中需要调查其地表形变状况而考虑是否饶避。

亢邈迒中铁第一勘察设计院集团有限公司,西安 710043摘要:煤矿采空区作为铁路选线的重要控制性因素,在线路设计中需要调查其地表形变状况而考虑是否饶避。

传统的人工调查、GPS、钻探等外业手段均是以点状测量为主,难以在短时间内获取大范围面状区域的地表形变状况。

因此,本研究采用遥感领域中的时间序列InSAR技术,通过获取2017年3月至2020年8月共51期的Sentinel-1雷达卫星数据,对铜川北区矿区进行地表形变监测。

结果表明,监测期间区域最大年平均形变速率绝对值不超过10mm/year,证明该区域没有明显地表形变。

关键词:滑坡监测;InSAR技术;SBAS-InSAR;Sentinel-1Deformation monitoring of Mining Area Surface in North Tongchuan Based on Time Series InSAR TechnologyKANG Miao-hang(China Railway First Survey and Design Institute Group Co.,Ltd. Xi’an 710043,China)Abstract: As an important controlling factor in railway route selection, mine goaf needs to investigate its surface deformation and consider whether to avoid it.The traditional field methods such as manual survey, GPS and drilling are mainly point measurement, which is difficult to obtain the surface deformation of a large area in a short time.Based on the sentinel-1 radar satellite data collected from March 2017 to October 2020, the SBAS-InSAR method of time series InSAR technology is used to identify the settlement deformation of mine area in North Tongchuan. The result shows that the maximum deformation rate is less than 10mm per year, proving the monitoring area is relatively stable.Key words:Railway line selection; InSAR technology; Goaf; SBAS-InSAR; Sentinel-1采空区作为矿区常见的不良地质,对铁路工程建设及运营安全具有十分严重的威胁。

时序_InSAR_技术在水库监测中的应用研究

时序_InSAR_技术在水库监测中的应用研究

第 2 期2024 年 4 月NO.2Apr .2024水利信息化Water Resources Informatization0 引言《河南省“十四五”水安全保障和水生态环境保护规划》提出 8 个方面的重点任务,其中防洪安全保障位于首位。

如今,河南省各市、县水利基础设施的建设和运营已经相当普及,强化防汛靶向监管、筑牢安全坝势在必行。

水库运维传统变形监测方法是在水库关键部位布设垂直和水平位移监测网,使用仪器设备获取高精度变形信息,但这种方法仅限于对预先埋设监测点的关键部位进行监测,难以实现大面积、高密度的变形监测,因此可能无法及时识别潜在的安全隐患。

此外,传感器主导的健康监测系统,多存在静力水准仪电子元器件损耗、能源供给异常和设备到期或即将到期等问题,可能导致坝体数据缺失。

相比传统坝体健康监测方法,时序 InSAR (合成孔径雷达干涉)技术可以开展面域监测,获取连续监测点,在反演损伤过程、分析变形原因、准确把握变形规律等方面具有极大优势,且可利用存档影像数据反演已发生过的变形过程,作为现有监测数据的补充。

时序 InSAR 技术为水库监测提供了新的解决方案。

V oege 等[1]验证了 SAR 干涉测量技术监测坝体变形的可行性;Cheng 等[2]成功实现了老挝溃坝变形反演并提取洪水淹没区,探究了基于 InSAR 技术的预防溃坝事故的早期预警方法;熊寻安等[3]使用时序 InSAR 技术实现了深圳市长岭皮水库土石坝表面变形的反演;Ruiz-Armenterps 等[4]反演了贝尼纳尔大坝 1992—2018 年的变形过程;徐东彪等[5]采用 InSAR 技术监测小浪底大坝变形,分析坝体中上部位移变化与水库水位变化的相关性;Liu 等[6]监测了西藏雅砻水库自 2014 年建成以来的变形,经综合分析提出 2017 年水库蓄水后,3 次滑坡变形都明显受到加速作用;Ruiz-Armenterps 等[7]使用中等分辨率 C 波段 SAR 数据再次确认了卫星雷达干涉测量法监测堤坝的适用性;杨星等[8]利用 Sentinel -1 数据,探讨 InSAR 技术监测水闸变形的可行性;姜龙等[9]对阿拉沟水库库区左岸变形进行 D -InSAR (差分干涉测量)的研究探索;张永荥等[10]以龙羊峡库区为研究区域,探索结合 BDS (北斗导航定位系统)和 CORS (连续运行参考站系统)数据的 InSAR 监测方案;王文衡等[11]为监测水库坝区的变形状况,利用水准数据对 InSAR 技术监测结果进行验证,表明 InSAR 结果具有一定可靠性。

基于时序InSAR技术的蔚县地面形变监测

基于时序InSAR技术的蔚县地面形变监测

基于时序InSAR技术的蔚县地面形变监测仝云霄;高井祥;陈宇【摘要】合成孔径雷达干涉测量(InSAR)技术在城市地表形变监测中具有高精度、高分辨率、低成本、空间连续监测等优势.以河北省张家口市蔚县为研究区域,获取了20景TerraSAR-X影像,分别采用PS-InSAR和SBAS-In-SAR两种时序技术得到了蔚县城区2015年6月9日到2016年1月4日期间地面平均沉降速率,结果表明,两种时序InSAR技术监测沉降结果具有很高一致性,而且相关性较高,达到0.9以上,城区中心发生了比较严重的地表沉降,形成了一个明显的沉降漏斗,沉降速率达到了30 mm/a,地下水的严重开采以及建筑物的加速构建是引起该区域地面沉降的主要原因,该研究可以为城市地面沉降治理、整体规划等提供参考意义.【期刊名称】《现代测绘》【年(卷),期】2019(042)002【总页数】6页(P1-6)【关键词】PS-InSAR;SBAS-InSAR;城市地面沉降;TerraSAR-X【作者】仝云霄;高井祥;陈宇【作者单位】中国矿业大学,江苏省资源环境信息工程重点实验室,江苏徐州221116;中国矿业大学,江苏省资源环境信息工程重点实验室,江苏徐州 221116;中国矿业大学,江苏省资源环境信息工程重点实验室,江苏徐州 221116【正文语种】中文【中图分类】TP722.60 引言地面沉降是指在一定的地表面积内不断加大对地下资源开发而产生的地面形变现象,是一种缓慢性地质灾害,可导致不可补偿的永久性环境资源损失。

地面沉降的产生与人类的各种经济活动密切相关,近年来,对地下水、石油、天然气、矿产资源的过度开采,以及隧道、地铁等地下工程的建设导致我国很多城市和地区出现了比较严重的沉降现象。

据统计,目前我国发生地面沉降的城市和地区大约有100多个,其中华北平原、长江三角洲以及汾渭盆地沉降较为严重[1],因此有必要加强对城市地面沉降的监测。

传统的形变监测方法主要有大地水准测量、GNSS测量和全站仪测量等,这些方法监测精度较高,在形变监测领域发挥了重要的作用,但是也存在着成本高、工作周期长、工作量大、无法获取空间连续信息等缺陷。

31合成孔径雷达

31合成孔径雷达

Unit 31 Interferometric SAR (In SAR)IntroductionRadar interferometry is a technique for extracting three-dimensional information of the Earth’s surface by using the phase content of radar signal as an additional information source derived from the complex radar data. It was first used in observation of the surface of Venus and the Moon.Graham was the first to introduce Synthetic Aperture Radar (SAR) for topographic mapping in 1974. There are two kinds of information which are required for the production of topographic maps. Firstly, the various objects and features to be mapped must be presented in an image with sufficient resolution to be identified. Secondly, a three-dimensional measurement of position, with respect to the platform, of a sufficient number of points must be made to define the terrain surface . The three-dimensional measurement can be made by SAR interferometry with a side-looking geometry from both airborne and spaceborne SAR sensors.A Synthetic Aperture Radar is an active sensor transmitting and receiving microwave signals, i .e . Measuring distances between the sensor and the point on the Earth’s surface, where the signal is backscattered. The sensor emits electromagnetic radiation (EMR) and then records the strength and time delay of the returning signal to produce images of the ground. The EMR involved can be imagined as a sine wave. Conventional SAR images are made up (as a raster) of the amplitude or ‘strength’of the sine wave — shown in images as grey level intensity values .When the sine wave starts to repeat itself (phase angle > 360 degrees) , one cycle of phase has occurred . If we collect two separate images from exactly the same satellite position (same range) , but at different times with nothing in the target area changing, one would expect the two sine waves from each image to be the same and in phase with each other .In practice, the position of the satellite between two image acquisitions is never identical, and the corresponding difference in the path (distance between satellite and ground) means there is a difference in phase between the two signals — a phase shift. The physical path difference can be expressed as an integer number of wavelengths plus the fraction of one wavelength. It can also be expressed as a difference in phase angle between the two signals. SAR interferometry makes use of this phase information by subtracting the phase value in one image from that of the other, for the same point on the ground. This is, in effect, generating the interference between the two phases signals and is the basis of interferometry .For the interferometric process to work successfully, a degree of similarity, or correlation must exist in the surface properties between the two image acquisitions . In most parts of the world, particularly temperate regions, correlation between images will degrade with time due to changing / moving vegetation, differing climatic conditions —termed’ temporal decor relation’. Correlation tends to remain good in arid, desert regions where little change occurs. An output from the processing chain is a coherence image, and this represents the correlation that exists between corresponding pixels of the two images —lighter pixels showing good correlation (e .g . arid, dry land cover ) , and darker pixels showing bad correlation (e .g . water, changing vegetation) .The phase value or angle (and hence phase differences in an interferogram) is not known absolutely, but is given in the range 0 - 360 degrees , i .e . the phase is wrapped onto a fixed range of angle of 0 - 360 degrees . In order to compute terrain heights and generate a DEM, theinterferogram fringes have to be unwrapped, i .e . the correct multiple of 360 degrees must be added to the phase difference at each pixel, the problem of solving this 2πambiguity is called phase unwrapping . If the ground were flat, unwrapping the above interferogram would produce an image of constant grey level.The interferometric data processing scheme includes in general ( 1) registration of the complex images, (2 ) the formation of the interferograms, ( 3) the phase unwrapping, and ( 4) the digital elevation model reconstruction .The basic use of SAR interferometry is to estimate topographic height .However, advancement on this technique can very usefully be applied to map surface displacements such as those associated with earthquakes, landslip or subsidence. Known as differential interferometry, the method uses SAR images of different dates that might span a surface displacement event. A first interferogram is created representing topography before the event and then a second interferogram created representing topography after the event. By subtracting one interferogram from the other, fringes that relate to common topography cancel each other out , so that remaining fringes should only represent a difference in topography, i .e . a displacement .OutlookCurrent research on SAR interferometry mainly focuses on the potentials of SAR interferometry by investigating the limiting factors of this technique. Ten years ago, the main difficulties were in the theoretical aspects, and scientists were waiting for access to suitable data sets in order to demonstrate the potential of SAR interferometry . Now, most of the theoretical aspects are reasonably well understood. At present, the main issues concern the operational constraints such as data availability, commercial software, manpower, and automation, adequacy of INSAR accuracy with regards to user, requirements and future system specifications. As mentioned before, one current research issue is the investigation of the influence of the atmosphere, which is assumed to have a significant influence on the quality of SAR interferometric data. The refraction can affect pixel misregistration and artefacts in the phase difference. The main problem during the data processing is the phase unwrapping. This part is still a problematical task and needs to be further investigated to reach an operational status. This is also one of the main reasons why commercial software packages for SAR interferometric data are still under development and not yet commercially available .Another major objective of current research is to produce high precision DEMs in an operational way. This is aimed at using SAR interferometric data from the ERS-1 / ERS-2 tandem mission. Reviewing the papers on SAR interferometry, it is clear that the basic techniques of producing interferograms, phase unwrapping, production of digital elevation models, etc . are now well studied and understood .Many organizations are now at the stage of making the software systems operational , and these should be available on the commercial market shortly . In terms of applications, many are reaching the stage of operational use, and other new applications on the use of SAR interferometry are being developed. The situation in a year or two will show a rapid increase in developments of SAR interferometry resulting from the increasing availability of ERS-1/ ERS-2 tandem mode data as well as operational airborne systems and other SAR satellite systems.。

多基线机载SAR联合相位定标两步法

多基线机载SAR联合相位定标两步法

多基线机载SAR联合相位定标两步法贺文杰;朱建军;付海强;王会强【摘要】多基线机载SAR数据相比单基线数据具有获取更丰富地表散射信息特点,已在地形测绘、植被监测领域广泛应用.在多基线数据应用前,需要对多基线数据集进行相位定标,校正数据集内由平台运动误差引起的相位偏差.本文提出一种多基线联合相位定标方法,解决传统永久散射体(Persistent Scatter,PS)相位定标方法无法剔除主影像残余运动误差(Residual Motion Errors,RME)的问题.利用德国宇航局机载E-SAR多基线数据对算法进行测试,试验结果表明,采用本文方法定标后,利用干涉相位估计的DEM的精度为2.30 m,优于传统相位定标方法解算的4.47 m,有效解决传统定标方法无法兼顾主影像运动误差的问题.【期刊名称】《测绘工程》【年(卷),期】2018(027)012【总页数】7页(P51-56,63)【关键词】相位定标;多基线;永久散射体;机载SAR【作者】贺文杰;朱建军;付海强;王会强【作者单位】中南大学地球科学与信息物理学院 ,湖南长沙 410083;中南大学地球科学与信息物理学院 ,湖南长沙 410083;中南大学地球科学与信息物理学院 ,湖南长沙 410083;中南大学地球科学与信息物理学院 ,湖南长沙 410083【正文语种】中文【中图分类】P228在机载SAR系统中,由于飞机平台在飞行过程中受气流摄动影响,会产生轨迹偏离和姿态变化等运动误差。

这种运动误差的补偿通常依赖于飞机的导航定向定位系统(POS),通过记录实时成像的位置参数及姿态参数,在成像过程中对SAR影像进行运动补偿[1]。

然而,现有导航设备精度指标无法满足运动补偿精度的需求,导致运动补偿后的SAR影像仍包含残余运动误差(Residual Motion Errors,RME)。

它会造成SAR影像出现散焦及相位波动[3],其在雷达视线方向投影产生的相位误差信号,如果不加以定标校正,将直接掩盖掉观测对象的细节相位信号,从而影响后续数据处理及相关应用。

insar术语中英文对照表

insar术语中英文对照表

以下是一些INSAR(干涉合成孔径雷达)术语的中英文对照表:
1. InSAR(Interferometric Synthetic Aperture Radar):干涉合成孔径雷达
2. SAR(Synthetic Aperture Radar):合成孔径雷达
3. Interferometry:干涉测量
4. Coherence:相干性
5. Phase:相位
6. Baseline:基线
7. Deformation:变形
8. Displacement:位移
9. Topography:地形
10. DEM(Digital Elevation Model):数字高程模型
11. LOS(Line of Sight):视线方向
12. Scatterer:散射体
13. Fringe:条纹
14. Unwrapped Phase:展开相位
15. Coherence Loss:相干性损失
16. Phase Unwrapping:相位展开
17. Ground Deformation:地面变形
18. Surface Displacement:表面位移
19. Differential InSAR:差分干涉合成孔径雷达
20. Persistent Scatterer InSAR:持续散射体干涉合成孔径雷达
请注意,这只是一些常见的INSAR术语的简单对照表,实际上INSAR领域涉及的术语还有很多。

如果您需要更详细或特定术语的对照,请提供具体的术语,我将尽力提供帮助。

地形复杂区域InSAR高精度DEM提取方法

地形复杂区域InSAR高精度DEM提取方法

地形复杂区域InSAR高精度DEM提取方法赵争【期刊名称】《测绘学报》【年(卷),期】2016(045)011【总页数】1页(P1385-1385)【作者】赵争【作者单位】中国测绘科学研究院,北京 100830【正文语种】中文【中图分类】P283干涉合成孔径雷达(interferometric synthetic aperture radar, InSAR)技术是合成孔径雷达(synthetic aperture radar, SAR)发展过程中极具里程碑式的成果,具有很强的发展和应用潜力,也是地球科学与遥感领域发展最迅速的技术之一。

SAR传感器的快速发展为InSAR技术提供了海量、快速、高分辨率的可用数据源,使得InSAR在地形测绘、地表形变监测、灾害监测、冰川运动、森林资源调查、农业监测和湿地变化制图等多个领域得到了广泛的应用。

但如何有效利用InSAR技术大面积高精度地获取地形复杂区域DEM仍是目前亟待解决的难点问题之一。

在地形复杂区域,InSAR提取DEM存在诸多问题:首先,SAR特有的侧视成像方式易在地形陡峭区域形成较大范围的阴影和严重的叠掩现象,导致影像信息缺失,从而引起提取的DEM出现漏洞;其次,在地形破碎区域SAR干涉影像对相干性差、DEM提取精度低,难以满足实际应用需求;再者实际生产中,影像覆盖范围大、地面控制点布设困难使得地形复杂区域InSAR提取DEM难度进一步增加。

本文针对上述问题,逐一进行了研究,并提出了相应的解决方案,研究内容主要集中在以下3个方面:①InSAR多侧视方向DEM的融合方法。

针对单侧视方向InSAR技术提取DEM中阴影叠掩所产生的高程信息缺失问题,研究阴影叠掩区域的检测与提取方法、地形复杂区域机载InSAR影像阴影影响去除方法及多侧视方向DEM融合方法,最终融合多侧视方向InSAR结果实现DEM的高质量提取。

②立体辅助InSAR方法。

面向地形复杂区域,充分发挥InSAR和立体SAR(StereoSAR)各自优势,研究在立体环境下提取或编辑种子点和结构线,协同相位解缠,指导解缠区域增长,提高解缠可靠性的方法,研究立体摄影测量协同改善干涉质量方法,研究多侧视方向SAR影像数据的立体、干涉联合自动提取DEM 的方法,构建StereoSAR和InSAR联合平差模型,实现立体DEM和InSAR DEM的融合。

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INTERFEROMETRIC SAR DEM CONSTRUCTION FOR LANDSCAPE PROCESSANALYSES IN NORTH-EASTERN ICELANDArto Vuorela(1) and Jukka Käyhkö(2)(1)Novosat LtdOpastinsilta 12 B, 00520 Helsinki, FinlandEmail: arto.vuorela@(2) Department of GeographyUniversity of Turku, 20014 Turku, FinlandEmail: jukka.kayhko@utu.fiINTRODUCTIONThis project concerns the origin and dynamics of the severe land degradation in north-eastern Iceland. The entire project (no. 187) within the ERS-AO3 scheme is titled “Environmental history of the severely eroded north-eastern Icelandic semi-desert – a multi-disciplinary approach utilising remotely sensed data combined with detailed investigations on palaeoecological, sedimentological and cultural aspects“. The sub-project described here aims at modelling the flood routes based on in-house produced InSAR digital elevation model (DEM). The modelling procedure can be divided into three main phases:•Producing a high-accuracy DEM based on SAR interferometry•Mapping the potential catastrophic flood routes using the DEM and a hydrological flow model in GIS environment.•Mapping the lava types, sediment cover on the lava fields, ancient flood routes and, specifically with the aid of ERS-SAR data, quantifying and correcting the effects of moisture and shadow responses in the opticaldata interpretation.This report describes the rationale of the project, the DEM production environment of Novosat and the DEM construction phase of this project. It points out some general limitations and problems of InSAR and aims at assessing potential methods and strategies for resolving them and to improve the accuracy.STUDY AREAThe study area is located in northern Iceland (Fig. 1). The region is characterised by severe erosion processes, volcanic activity, glaciofluvial processes and aridity due to the Vatnajökull rain-shadow. Most of the area is completely devoid of vegetation, whereas shrub heaths occupy the northern fringes along the coast. The area serves as a type example of an enigmatic region, where it is difficult to assess to what extent ecosystem changes or processes take place naturally, or as consequence of human interference.Catastrophic floods (jökulhlaups)Volcanic phenomena are common along the Northern Rift Zone of Iceland. Vigorous volcanic activity occurs beneath the Vatnajökull ice cap to the south of the study area [1] producing recurrently widespread tephra layers as well as catastrophic floods (jökulhlaup Icel.) [2]. Two major volcanic centres lie beneath the ice: the Barðarbunga and the Grimsvötn volcanic centre, both of which exhibit large subglacial caldera depressions. The Grimsvötn volcanic centre is the more active of the two with an eruption frequency close to one eruption per decade. The eruption in September-Data provided by ESA. Financial support provided by Jenny and Antti Wihuri Foundation and the British Council.November 1996 resulted in a catastrophic flood of nearly 4 km3 of meltwater, which covered the uninhabited Skeiðarársandur region in the south (c.f. Fig. 1). The flood caused substantial material damage by destroying roads and bridges with an estimated cost of 10-15 million USD [3].Had the October 1996 eruption taken place slightly further north, water would have resulted in jökulhlaup on the northern margin of the Vatnajökull [3]. The fact that traces of the meltwater from the 1996 eruption were detected in the northern rivers illustrates how close meltwater was to draining in a northerly direction. It is widely acknowledged that the Vatnajökull area is entering a period of renewed volcanic activity [3]. It is therefore probable that future eruptions will drain northwards posing a considerable hazard to communities. Based on preliminary investigations, it is hypothesised [4] that ancient catastrophic floods may have triggered some of the present day environmental (erosion) processes in the study area.Landscape evolutionIn addition to interferometry, ERS-SAR data allow determining the aerodynamic roughness of lava surfaces, which is an important parameter in studies of sand transport. Smooth pahoehoe lavas act as significant pathways for aeolian transport, whereas rough aa lavas appear sediment sinks, and barriers for the advancing sand. There are no previous maps of lava surface roughness in Iceland (or elsewhere) and the research group is in the process of developing new techniques in this field. An attempt will be made to divide lava flows into relative age classes by using remote sensing data. In addition, the techniques applied here will allow estimates of the impact of lava flows on drainage systems and vegetation. Thorough mapping of the surface roughness will also allow more accurate sand transport prediction, being of great aid in land reclamation.The DEM will be used in estimates of eruption volumes. The calculations will be refined with morphological measurements in the field (thickness and width of lava flows). The research group is aware of the many complications in the calculation of volcanic eruption volumes [5], [6]. Therefore, many of our results on the eruptive volumes will be given as order-of-magnitude estimates rather than in absolute figures. These estimates will then be used in assessing environmental stresses and landscape evolution. The digital elevation model will also be used in tracking current and ancient routes of jökulhlaups. The elevation model can also be used in prediction of likely routes of future lava flows and jökulhlaups and is therefore useful in hazard assessment. Finally, the data on the relative and absolute age lava flow fields, volume calculations of eruptions and mapping of the extent of flow fields allow us to construct a general model on the landscape evolution of the area.Fig. 1. Map of Iceland showing the active volcanic zone, the largest ice caps and the borders of the four ERS tandem pairs applied so far in the DEM construction.MATERIAL AND METHODSERS-SAR dataFour ERS-1/2 tandem SLC image pairs were employed in the DEM construction. In the first phase, images with snow cover on the ground (October-February) were ordered (Table 1). Later, a set of snow-free images was ordered, as the coherence of the latter proved to be better. See Discussion for further details on the problems of the material selection. Topographic maps on scales 1:50 000 and 1:100 000 were employed in the combining of the individual models. The 1:50 000 map set did not cover the whole study area. Furthermore, the projection and datum of the 1:100 000 maps were inconsistent with the other data, and were used mainly for elevation assessment.Table 1. The ERS tandem pairs processed. The highlighted images showed poor coherence and were discarded after pre-processing (see text for details)SAT ORBIT FRAME SHIFT ACQ.DATEE124045225909960219E204372225909960220EI240452295-2960219E2043722295-2960220E1275441305-2961020E2078711305-2961021E1275441323-2961020E2078711323-2961021E1217692277-1950913E2020962277-1950914E1217692295-1950913E2020962295-1950914MethodsThe interferometric processing was carried out by Novosat Ltd, Helsinki, Finland. The software employed in the process is an in-house product, implemented by Dr. Einar-Arne Herland at the Remote Sensing Group of the Technical Research Centre (VTT) of Finland [7]. The modus operandi is based on the long-term research on SAR data and techniques at VTT, and the method was implemented in collaboration with Novosat. Currently, the software is in operational use for commercial projects, and Novosat is marketing large-area DEM’s based on ERS SLCI data. Comparison procedures against a 25 m raster DEM extracted from a 1:20 000 topographic map (National Land Survey of Finland) have revealed that the InSAR elevation models show typically a vertical accuracy of 5–15 m [8]. In South Finland, a 4 metre RMSE has been accomplished previously.The software offers highly automated pre-processing of the data. Correlation, slave interpolation, fringe and coherence calculation, fringe filtering, breakpoint and contour generation are carried out automatically. For phase unwrapping, fully automated - but also less reliable - methods were not employed. Instead, unwrapped phase is manually integrated over the total interferogram area, based on branch and cut methods (Fig. 2). Various tools help in this task; e.g. the coherence image is used as a threshold mask and can be viewed in parallel during the work. In addition, the slant and map rectification and advanced joining of the independent models are conducted. The interface has been designed as user-friendly, and the icon-based alternatives offer in-built state-of-the-art InSAR techniques. Due to general limitations of SAR interferometry, Novosat has lately also adapted radargrammetry processes as a complementary alternative for DEM production.Fig. 2. A colour image, showing the unwrapping of a relatively good-coherence interferogram. Lakes, wetlands and the instrument side of the hills show low coherence. The coherence image can be used as an aid to conduct the unwrapping. The fringe disconnections and the extra fringes on the instrument side of hill can be interpreted and drawn with the blue mask area, remaining wrapped. These hollows can be interpolated later. The yellow mask is the so far unwrapped area. Coherence images can be also used in weighting the DEM mosaicking as well as in thresholding the blue mask for the unwrapping.Interferometric coherence images are orthorectified and hence, can be used for land cover analyses in combination with optical satellite images. A relative presentation of the DEM reliability can be yielded with the aid of a variance image from the joined models.RESULTSAfter the data selection and the subsequent careful analysis of the available data, a relatively satisfactory elevation model was produced using two winter image pairs and two snow-free pairs (Fig. 3). The final model shows spatially variable variance and some holes on the western edge (c.f. Fig. 3), in location where there were lines missing in the original data. The image pair margins are visible in some parts of the mosaic (see Discussion and Fig. 5), indicating that the match between the individual models is not perfect. Based on mere GCP measurements, an individual model may still tilt about 1:1000, causing a threshold of a few tens of metres. Further adjustments are being carried out to flatten out these thresholds at the individual model margins, by adjusting the tilt of whole individual models. Fig. 4 shows a potential application of the DEM for interpretation of land cover types in combination with a TM image.Fig. 3. The DEM mosaic of the study region at its current state. The bright area in the south is the Vatnajökull ice cap, which contains elevations well over 1000 metres. At the glacier margin, wet snow and sediment plus melt water streams give rise to poor coherence and hollows in the model. Lake basins such as Askja caldera (the semicircular spot in the middle of the lower half of the figure, surrounded by highlands) appear also as discontinuities. The Upper Pleistocene mountain arc to the right fringes the Volcanic rift zone and acts as a dam to the potential flood water bursting from the south (see also Fig. 4).Fig. 4. A perspective view towards the northwest with Landsat TM image (432 RGB) draped over the InSAR DEM. Red colour denotes vegetated surface. Askja caldera with snowy rims is visible on the left. The ash (tephra) layer (in pale blue) originated in the 1875 eruption at Askja. Subsequent floods have disintegrated the tephra deposit in the east.A black lava flow from the 1961 eruption sits on the northern rim of Askja. In the middle of the scene rises a table mountain Herðubreið (1682 m). Perspective images with shading are particularly sensitive in revealing any artefacts in the DEM. With the given illumination angle, the threshold between the two individual models appears as a dark linear band crossing the view from east to west (see text for further details).DISCUSSIONImage pair selectionSeason and weather effectsCareful InSAR image selection turned out to be a crucial step in the DEM production. Based on earlier experience from northern Fennoscandia (Lapland) [8], it was assumed that the seasonal snow cover would have a stabilising effect on e.g. the vegetated areas and hence, data from wintertime would show good coherence. Therefore, baseline values and weather conditions during the wintertime ERS-1/2 tandem flights were assessed at first instance, and successive acquisition dates showing as little change as possible in the temperature and wind conditions were selected. A request was also made to ESA to obtain an access to the Interferometric Quick Looks (IQL's) [9] in order to help assessing the data quality before placing the order. There were, however, no IQL’s available for our candidate pairs. Only after the scenes had been received and processed, an offer was made to the authors about testing the IQL system with data stored at the UK-PAF. IQL processing is subject to start there in January 2001.The quality of the wintertime images proved unsatisfactory and subsequently, severe problems were encountered with the interferometric coherence. Together with some pitfalls in the project funding, these caused delay in the analyses. The first part of the project was carried out in 1998–1999 with four SAR SLCI image pairs requested and received. Only two of the pairs qualified for further processing. The two other image pairs demonstrated too low coherence and were consequently not used (c.f. Table 1). Low coherence is believed to be due to the highly variable weather and snow conditions in the area. The absolute elevation rises from sea level to well over 1000 m in the south, creating a steep spatial gradient in both temperature and humidity. At high altitude, snow may be dry, whereas in the warmer coastal region the water content of the snow may be distinctively higher. High cyclone activity in North Atlantic induces variable climatic conditions in Iceland during winter and therefore, it is practically impossible to find suitable tandem acquisitions with stable weather conditions. Unfortunately, the image pair, which showed the best coherence for winter images, had some bad (missing) lines, resulting to a section of poor coherence in the final DEM (this problem was, however, recognised before placing the order, but could not be avoided due to the initial wintertime constraint). In the year 2000, two snow-free pairs (acquired in September) were ordered and processed, and the models were finally combined.Based on the advice from ESA [10] and the experience described above, late summer images seem to offer a better option for InSAR in Iceland. This is controversial with the situation in Fennoscandia, where winter images typically show better coherence. It is hypothesised here that one should avoid highly variable weather conditions (e.g. Oceanic subarctic and subantarctic areas during the hemispheric winter) and instead try to find the most stable season for data acquisition. It is obvious that arid areas devoid of vegetation show a good coherence on snow-free pairs, but these images showed a good coherence also for the Icelandic shrubs. Holes in the models cannot be thoroughly avoided as with the global ERS tandem data, the coherence is only rarely good enough throughout the whole scene area for the production of a reliable elevation model. Some of the gaps are so large that interpolation is not feasible.Number of image pairs employedAnother option for improving the quality of the final DEM may be an employment of a large number of image pairs from the same area. In order to identify and eliminate any atmospheric artefacts, it is recommended to use three to four overlapping pairs from both ascending and descending tracks, as this gives better results than using only one or two pairs.Ground control points and mosaickingThe horizontal accuracy of the ERS-SAR data is initially about 100 m throughout the world when using the PRC orbit products. Therefore, in principle, a single elevation point will suffice the georeferencing, without a need for additional GCP's. However, by incorporating GCP’s from maps and other databases, the horizontal accuracy can be improved to 20-30 metres (although identifying and measuring representative points on a DEM can prove difficult). Atmospheric artefacts may cause difficulties, as a GCP measured at an artefact may result to a tilt in the model. Hydrological models are most demanding applications and therefore, additional relative points can be measured to smoothen the edges as much as possible. In Fig. 5, the image threshold effect is no more systematic and is therefore presumably due to atmospheric artefacts and varying coherence within an image, rather than a true mismatch in model rectification. Designing a procedure where the GCP’s could be measured directly on the initial images might prove beneficial.Fig. 5. An example of the effect of atmospheric artefacts at a model border. The threshold between models runs diagonally across the scene from upper left to lower right, indicating an elevation difference of up to several tens of metres. Any systematic incompatibility has already been removed from the mosaic by adjusting the tilt of the models. Thus, the remaining (non-systematic) mismatch is presumably due to atmospheric artefacts and varying coherence rather than a rectification error.InSAR-based elevation models have a good cost/benefit ratio compared to traditional photogrammetric methods, but one has to acknowledge the fact that the application cannot be employed readily in all regions. In addition to the land cover types known to suffer from limited C-band coherence (e.g. rain forests) and atmospheric artefacts (e.g. deserts), also areas in the Subarctic with steep climatic gradients and variable weather conditions may bring about difficulties in DEM construction.The next step in the project at hand is fine-tuning the DEM by adding more GCP's and using more image pairs. Thereafter, the DEM will be employed in the modelling of catastrophic flood routes with the aid of GIS techniques. Of specific interest with regard to the modelling exercise will be the high momentum, variable viscosity (due to high sediment load) and the point-source (either single or multi) character of the floodwater bursting from the ice sheet margin. Therefore, a normal hydrological modelling, such as described in e.g. [11] will most probably prove insufficient.CONCLUSIONSBased on the case study reported here, the following conclusions can be drawn with regard to the interferometric DEM construction:1.In the current study region, snow-free image pairs show better coherence than winter pairs, and are thereforesuggested for DEM construction even in moderately vegetated areas. In Boreal forest region, however, the coherence is generally better during the snow period.2.Areas with steep climatic gradients and highly variable weather conditions (e.g. melting/re-freezing snow) degradethe image coherence and prove to result in problems in the analyses of the data acquired in ERS tandem flights. 3.The use of only two overlapping image pairs and a limited number and distribution of GCP's failed to produce atotally threshold-free DEM mosaic. Increasing the number of overlapping pairs from 1-2 to 3-4 will improve the total DEM quality. Adjacent models can also be fit to one another, or to a reference DEM, where available.4.An access to the IQL’s prior to placing a data order would be of great benefit as otherwise, the only way to assessthe applicability of a pair is to purchase and pre-process it. If the IQL’s are not available, it is advisable to order justa single image pair from a particular track at first hand, to allow preliminary investigation of the coherence. Theavailability of IQL's will improve in the near future, as the data from Northern Europe will be processed from January 2001 onwards at UK-PAF.ACKNOWLEDGEMENTSThis study would not have been possible without the close collaboration of the whole research team: P. Alho (Turku), O. Arnalds (Reykjavík), J. Hendriks (Turku), M. Rossi (Turku), A. Russell (Keele), H. Seppä (Uppsala) and G. Wiggs (Sheffield). The authors thank T. Ikola (Novosat) for fruitful comments on the manuscript and E. Forsvik (Novosat) for invaluable assistance in image processing.REFERENCES[1] G. Larsen, M.T.Gudmundsson, and H Björnsson, "Eight centuries of periodic volcanism at the center of the Iceland hotspot revealed by glacier tephrostratigraphy, " Geology, 26, pp. 943-946, 1998.[2] H. Björnsson, "Jökulhlaups in Iceland: prediction, characteristics and simulation, " Ann. Glaciol., 16, 95-106, 1992.[3] M.T Guðmundsson, F. Sigmundsson, and H. Björnsson, "Ice-volcano interaction of the 1996 Gjálp subglacial eruption, Vatnajökull, Iceland, " Nature, 389, pp. 954-957, 1997.[4] J.Käyhkö and P. Worsley, "Sediment distribution and transport processes on Holocene lava fields in north-eastern Iceland, " Supplementi di Geografia Fisica e Dinamica Quaternaria, Supplemento III, Tomo 1, p. 226 (Abstracts of the Fourth International Conference on Geomorphology, Bologna, Italia, 28.08.-03.09.1997).[5] M.J. Rossi, "Morphology and mechanism of eruption of postglacial shield volcanoes in Iceland. " Bulletin of Volcanology, 57, pp. 530-540, 1996.[6] M.J. Rossi, "Morphology of the 1984 open-channel lava flow at Krafla volcano, northern Iceland", Geomorphology, 20, pp. 95-112, 1997.[7] E.-A. Herland and A. Vuorela, "Operational DEM generation by means of SAR interferometry", IGARSS’97 Proceedings, Singapore, 03-08 August, IEEE1997, Volume III pp. 1344–1346, 1997[8] A. Vuorela, E.-A. Herland, and A. Saarikoski, "SarDEM – Production Process and Applicability of SarDEM Digital Elevation Models Based on Interferometric ERS SAR Images", Report, National Land Survey of Finland 1998. 50 p.[9] "INSI SAR Interferometry Sample Image Browser", (ERS-1 and ERS-2 Tandem Interferometric Quick-Looks), http://earth.esa.int/insi, ESA, 2000[10] D. Massonnet, CNES, pers. communication, 20 January 1999[11] A. Walker, Using the LANDMAP British Isles 1" IfSAR DEM for Hyrdographical Network Derivation. ERS -ENVISAT SYMPOSIUM, 16 - 20 October, 2000。

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