引用本文
郭华东, 吴文瑾, 张珂, 等. 新型SAR对地环境观测[J]. 测绘学报，2022，51(6)：862-872.
DOI: 10.11947/j.AGCS.2022.20220098 
GUO Huadong, WU Wenjin, ZHANG Ke, et al. New generation SAR for Earth environment observation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 862-872.
DOI: 10.11947/j.AGCS.2022.20220098
新型SAR对地环境观测
1. 可持续发展大数据国际研究中心, 北京 100094;
2. 数字地球重点实验室, 中国科学院空天信息创新研究院, 北京 100094;
3. 中国科学院大学, 北京 100049
2. 数字地球重点实验室, 中国科学院空天信息创新研究院, 北京 100094;
3. 中国科学院大学, 北京 100049
摘要：合成孔径雷达(SAR)系统在对地观测中具有全天时全天候的独特优势。近十几年来, 多模式、多角度、多维度、大幅宽、高分辨率、多基协同等SAR技术的问世, 代表着新型SAR观测时代的到来。为对这一SAR发展阶段的特点和能力进行分析, 本文首先介绍了新型SAR系统观测能力的发展, 包括如何获取大范围、多时相、多层次SAR综合对地观测数据及实现月基SAR等观测技术; 然后, 总结了杂交介质建模、时频分解、深度学习、压缩感知等新型信息提取方法在SAR领域发挥的作用; 最后, 介绍了新型SAR在城市管理、植被调查、极地与海洋测绘以及灾害监测等领域的研究进展, 旨在推动SAR观测技术在测绘领域更广泛而深入的应用。
关键词：合成孔径雷达 对地观测 测绘
New generation SAR for Earth environment observation
1. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China;
2. Key Laboratory of Digital Earth Science, Aerospace Information Institute, Chinese Academy of Sciences, Beijing 100094, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China
2. Key Laboratory of Digital Earth Science, Aerospace Information Institute, Chinese Academy of Sciences, Beijing 100094, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract: Synthetic aperture radar (SAR) systems have the unique advantage of all-time and all-weather observation. In the past decade or so, with the continuous announcement of multi-mode, multi-angle, multi-dimension, wide-swath, high-resolution, and multi-static SAR observation technologies, SAR observation has been in the "new generation" stage. This paper first introduces the development of the observation capability of the new generation SAR, including how to realize the wide-swath, multi-temporal, and multi-level observation as well as moon-base observation; Then, the development and application of new information extraction methods such as the non-stationary statistical modeling, time-frequency decomposition, deep learning, and compressed sensing for new generation SAR images are summarized; Finally, recent application progress in urban management, vegetation mapping, polar region and ocean survey, and disaster monitoring are listed to promote wider and deeper applications of the new generation SAR.
Key words:
SAR earth observation surveying and mapping
合成孔径雷达(synthetic aperture radar, SAR)系统具有不受天气、日夜影响的优良特性，因而在实时地表信息获取、多云雾地区测绘、时序动态分析等方面扮演着重要角色。自1957年密歇根大学采用机载平台获得第一张SAR影像之后，半导体技术、数字技术和计算机技术快速发展，SAR系统和信息提取技术也获得了持续更新[1]。截至目前，SAR对地观测技术已经经过了4个阶段的更迭[2]，其中近十几年来以多模式、多角度、多维度、大幅宽、高分辨率、多基协同等技术为代表的发展阶段被称为第4阶段SAR或新型SAR。这同时也是SAR影像大量由军用走向民用、由商业走向免费、用户数量和应用范围大幅激增的重要阶段。在此背景下，本文从SAR对地观测技术、信息提取技术及应用3个方面总结了这一个发展阶段中SAR的特点和能力，展望了其发展趋势以及在测绘领域可发挥的作用与优势，以期推动SAR在测绘领域的发展。
1 新型SAR观测技术2010年以来，中国高分三号[3]、天绘二号[4]，欧空局(ESA)Sentinel-1[5]、BIOMASS[6]，德国TanDEM-L[7]，加拿大RADARSAT Constellation Mission (RCM)[8]等星载SAR任务的预定和运行，以及各类先进机载SAR设备的问世标志着新型SAR对地观测系统的蓬勃发展。这一阶段SAR系统普遍具有大幅宽、高分辨率、多维度甚至多角度观测能力，月基SAR等概念系统的理论模型也逐步完善，在硬件层面为大范围、多层次、精细对地观测信息的快速获取奠定了基础，这将为大面积高精度地表测绘提供极大便利。
1.1 大幅宽观测大的测绘带宽是实现大范围宏观信息获取及高频率重访的必要要求。为解决SAR在观测宽度和分辨率方面对系统设计参数需求中的矛盾[9]，研究人员提出了多种观测波束拓展和重构技术。方位向多波束通过在方位向发射多个窄带波束或分解宽波束再重构信号，实现方位向高分辨率宽幅观测[10-13]，此类技术已在高分三号系统中得到应用。变脉冲重复频率通过周期性改变脉冲重复频率，将集中的回波盲区分散在整个成像带内[14]，从而对距离向连续成像，形成距离向大带宽。俯仰向数字波束合成可在宽波束信号的同时实现高增益以提高系统信噪比，目前在机载系统中得到了论证。多发多收技术通过更多的收发端在高分辨率宽幅成像的同时实现多模式协同，已获得试验论证[15-17]。随着这些技术的落实及推广，SAR系统的重访能力和信噪比仍有同步提升的空间，这将进一步减轻影像拼接、去噪等处理任务，提高对地球动态信息的追踪和分析能力。
除改变波束策略以外，提高轨道高度也是增大测绘带宽的重要手段。SAR卫星系统轨道一般在2000 km以下，而目前已有研究表明，地球同步轨道卫星(高度约3.6万km)可通过其与地球之间的相对运动形成合成孔径，从而满足SAR成像原理，理论上具备小时级重访能力、甚至可通过设计脉冲重复频率变化实现凝视观测[18]，预期可为台风登陆、火山爆发、地震等灾情监测带来新机遇。
1.2 高分辨率及毫米波观测高空间分辨率可显著增强影像的描述能力，提供更丰富的地物细节并有助于小型地物的识别和分析。目前多数机载SAR系统及SAR-Lupe[19]等星载SAR系统已实现亚米级空间分辨率。其中在距离向的实现主要依靠增大信号带宽，在方位向则可通过聚束和滑动聚束模式增加合成孔径积累时间。
特别地，毫米波因多方面优势成为越来越多高分辨率SAR系统的选择[20]，包括：工作频率高(30~300 GHz)，易获得大带宽和距离向高分辨率；天线尺寸小，易小型化；干涉基线短，同条件下高度向分辨率更高；穿透弱，影像轮廓清晰等。目前毫米波SAR系统在机载平台已经非常成熟，如德国的MEMPHIS系统[21]，美国Sandia实验室的Ka SAR[22]，中国北京无线电测量研究所的CAMSAR[23-24]等。相比之下，星载毫米波SAR报道较少，我国齐鲁一号搭载Ka波段SAR于2021年成功发射，美国的表面水及海洋地形任务(surface water ocean topography, SWOT)将搭载Ka波段双站干涉SAR(Ka-band radar interferometer, KaRIn)，用于获取厘米级精度的海洋表面高度[25]，为海洋测绘开启新篇章。
1.3 多角度观测多角度SAR系统可从不同方位角对地表进行观测，获得地物的各向异性信息，一定程度上消除阴影和叠掩，还可对散射体的高程进行解算[26-27]，具体可通过斜视扫描、宽方位波束观测、圆迹观测等方式实现。斜视成像算法的成熟为大斜视扫描奠定了基础[28]，使得SAR系统能通过改变斜视角(波束的方位指向)对平台前后区域成像，从而获得多角度信息。宽方位波束SAR具有较大的方位向视角，可通过子孔径分解获取地物的多角度信息[29]，因其能获得的角度范围有限，主要用来识别各向异性地物，可用于实现自动化的建筑物分布提取[30-31]。圆迹观测将观测波束固定指向感兴趣地物，通过平台绕飞获得360°的观测信息，旨在实现高分辨率三维信息重建[32-34]。此外，还可通过正交或反向飞行实现多角度观测，在道路等方向性地物的提取中优势明显[35-36]。
1.4 多星/多基协同观测近年来发射的大型星载SAR系统多为多星模式，小型商业SAR星座也在快速发展，芬兰ICEYE[37]、美国CapellaSpace[38]、日本Synspective[39]等公司均已布建SAR卫星星座，我国的海丝[40]、齐鲁小卫星星座中的首颗SAR卫星也已发射升空。多星组网可实现更广泛的覆盖能力和更短的重复周期，如Sentinel-1重访周期由单星的12 d降低至双星6 d[41]，加拿大雷达卫星星座任务(RCM)能够对北极地区每天重访4次[8]，TerraSAR-X/PAZ卫星星座具备全球单日重访能力。此外，将SAR系统的发射和接收天线分设于多个平台上可构建多基协同观测[42-44]，利于实现长基线及不同长度基线的干涉测量，从而获得更好的高度向测量精度或动目标分析能力[45-46]。多星/多基协同系统的蓬勃发展将大力促进SAR系统在三维/四维信息获取方面的应用，提高地形测绘精度。
1.5 月基SAR观测人造卫星和机载平台只能对地球上有限区域进行短暂观测，信息获取的宏观性和连续性受到限制。月球是地球唯一的自然卫星，是提供长期、稳定对地观测的最佳选择。相对于星载SAR，月基SAR的优势包括：测绘带宽高于星载SAR一个数量级；可通过分布式SAR形成长期稳定的干涉基线，获取比星载SAR高一至两个量级精度的地形和形变测绘能力；通过合理维护可实现远高于星载平台的工作寿命，提供长期一致的校准数据等[47-49]。近年来，有关学者在月基SAR原理及关键技术方面开展了丰富的研究。在系统方面，对单/多站月基SAR观测几何[50-52]、信号历程[53-54]、系统参数特征[55]和选址[56]进行了分析；在成像方面，论证了地球曲率和月球公转效应对成像的影响，并开展了成像仿真[57-58]；在图像处理方面，提出了月基SAR辐射定标和几何校正方法；在应用方面，提出了针对固体潮形变的月基重轨及多基线干涉方案[59-61]。月基SAR观测的实现预期将为大尺度、长周期地表观测开拓更多的发展空间[62-63]。
2 新型SAR信息提取技术分辨率的提高使得SAR影像所包含的地物细节极大丰富，但同时也导致影像散射机理特性改变。在高分辨率SAR影像中，地物通常表现为点、线等在空间上不连续的零散结构，这使得很多传统图像处理方法不再适用，加之新的观测模式为更丰富信息的获取提供了可能，新型SAR信息提取方法和技术大量涌现。
2.1 杂交介质统计建模斑点噪声是SAR影像的固有特性，受其影响，SAR影像上一个像元的散射强度并不能直接与该点地物的后向散射系数相对应，因此统计建模是SAR影像描述的重要手段。传统SAR统计建模中认为地表在小窗口范围内是均质的，即不存在主导性散射体[64]。而随着分辨率的提高，这一假设不再成立。研究人员提出很多描述强散射中心叠加均质地物的杂交介质模型，包括基于Rician分布[31]，复广义高斯分布(CGGD)[24]、广义gamma分布[65]，stable-Rayleigh分布[66]，广义高斯Rayleigh分布[67]的方法等，并在高分辨率SAR影像滤波[68]、分割分类[69]、边缘提取[70]、地物识别[71]等任务中发挥了重要作用。
2.2 时频分解时频分解通过构建一个时间和频率的二维联合分布来分析统计量随时间或空间改变的信号[72]，是挖掘SAR影像中地物各向异性及运动信息的重要手段[73-75]，还可进一步被用于成像优化[76-77]及变化检测中[78]。近年来，针对宽幅SAR中的信号干扰问题，研究学者提出了基于低秩稀疏矩阵分解的时频分析方法[79]。在时频分解中结合极化信息及深度学习技术为主要的发展趋势，相关方法在图像分类、船只等目标检测等方面体现出了优异的性能[80-82]。目前在SAR领域中应用的时频分析方法还主要局限在短时傅里叶变换，它通过对SAR影像的二维傅里叶频谱进行加窗，再截取每个窗口频谱变换到空域形成子孔径图像[83]。但该方法存在时间和空间分辨率无法同时提高的问题，会限制信息提取的精度。时频窗口更为灵活的分解方法在SAR领域中还有待应用和发展。
2.3 深度学习分辨率提高带来的SAR影像场景复杂性加大使得传统机器学习和人工制作特征带来的局限性日益明显。深度学习通过非线性地联合数十甚至数百层神经网络自动实现特征提取和筛选，在近年来得到飞速发展。基于深度学习的图像信息提取方法通常以卷积神经网络(convolutional neural network, CNN)为核心，对多层次的上下文信息进行自主挖掘和集成，从而同时实现图像的精细描述和高度抽象。在SAR领域中，深度学习技术已被成功用于复杂场景分类、图像分割、像素级平滑分类、特殊目标检测等处理中[84-86]。特别地，针对SAR原始数据的复数特性及极化、干涉等特色信息利用的SAR专用深度网络也被相继提出，显著提升了SAR信息提取的智能程度和稳健性[87-91]。然而，受限于现有SAR数据标注样本的体量和构建难度，深度学习在SAR领域中发挥的作用还非常初级，弱监督甚至无监督的方法模型亟待发展，并正在成为这一领域主要的发展趋势。
2.4 压缩感知压缩感知(compressed sensing, CS)[92-95]是一种稀疏信号采样及重构方法，即可以在避免信号损失的前提下用远低于奈奎斯特采样定理要求的速率进行信号采样与重构。它采用一个与稀疏基不相关的感知矩阵，将高维信号投影到一个低维空间上，然后通过求解一个优化问题，从少的投影中以高概率重构出原信号[96]。目前压缩感知技术已广泛应用于SAR影像的超分辨率成像和三维、四维层析成像中，如建筑物三维结构高精度反演[97]、森林垂直结构重建[98]等工作。目前主要的发展趋势集中在顾及散射机制的极化层析、永久散射体差分层析[99]、联合压缩感知与深度学习的三维超分辨率成像[100]以及阵列前视成像[101]等方向。相关技术的发展将大力支撑对存在垂直结构地物的精细信息提取。
3 新型SAR的应用新的SAR观测模式和信息提取手段带来了更丰富、更高精度的信息获取能力，为SAR系统在城市管理、植被调查、极地与海洋测绘、灾害监测等领域的发展带来了更多机遇。不受云雨影响的高重访周期、超宽幅成像、媲美光学影像的平滑地物分类、高度维精细成像等新特性使SAR系统得到越来越广泛的应用。
3.1 城市管理基于SAR数据的城市土地覆盖/利用分类及三维/四维信息提取在城市规划、灾害预警、数字孪生等城市管理工作中发挥着重要作用。在土地覆盖/利用信息获取方面，研究人员通过极化SAR深度神经网络实现了详细的城市地物分布测绘[102]；结合改进的差分图像和残差U-Net网络可实现高敏度城市建筑物变化检测[103]；毫米波SAR影像和毫米波特色特征描述集为城市精细地表信息获取提供支持[104]。在三维/四维信息提取方面，基于大地测量层析成像框架实现了厘米级绝对定位精度的城市三维重建[105]；基于高分三号SAR数据和差分层析成像技术探测到了毫米级的建筑物形变[106]；广义差分成像模型实现了建筑物的线性运动、季节性运动等多个形变速率的精确反演[107]；永久散射体层析成像被应用于多个城市建成区的高精度连续形变监测[108]。这些新型SAR的应用正快速推动着城市管理的数字化和精细化进程。
3.2 植被调查SAR在植被调查中被用于植被分类、高度反演、垂直结构分析、生物量统计等多个方面。如研究学者将Sentinel-1数据与原位调查结合，绘制了欧盟大陆首张10 m分辨率作物类型分布产品[109]；使用多种高分辨率SAR卫星观测数据联合估计了全球森林地面生物量[110]；通过联合Sentinel-1与Sentinel-2对全北极植被高度进行了制图[111]等。特别地，由于长波SAR对植被冠层的穿透性，SAR数据相比光学数据在生物量反演方面具有天然优势，并可突破光学模型中的高生物量饱和问题。欧洲航天局的BIOMASS计划将搭载P波段SAR于2023年发射以提供全球森林地面生物量数据[112]，通过联合L和P波段的生物量反演模型可显著提高热带森林生物量反演饱和点[113]。此外，结合极化SAR对具有不同散射机制的散射体进行解混可对树木不同部位的信号进行分离，从而实现树木垂直结构的精细提取[114]。基于SAR数据的植被参数反演已逐渐成为林草、农业资源评估及碳动态监测的一个关键途径，对热带亚热带等常年多云雨地区的植被遥感调查尤其具有重要意义。
3.3 极地与海洋测绘SAR在极地测绘中主要应用于海冰分类、冰面冻融监测、冰面特征监测、冰盖运动测量等方面，在海洋测绘中主要用于对海面风场、海浪、海流以及海洋污染事件等进行监测。由于极地与海洋研究通常涉及面积大、空间范围广，对数据的幅宽提出了很高要求。新型SAR普遍具备ScanSAR模式，观测范围可达数百公里[115]，可有效减轻影像拼接工作量并提高结果图的完整性及时空一致性。多项研究使用Sentinel-1 Scan SAR数据生成了高时空分辨率的南极冰盖冻融及南、北极海冰分类数据集[116-118]。此外，研究学者还基于SAR数据生产了冰流速[119]、冰缘湖分布[120]、冰裂隙分布[121]等极地环境关键要素专题产品。这些研究成果将有助于我们量化极地环境对气候变化的响应、支撑对冰川动力学及地球系统过程的进一步理解。在海洋监测方面，多种物理和经验模型以及极化、干涉测量等技术被用于对海面风场[122-124]，海浪[125-126]和海流[127-129]参数进行反演。我国于2020年12月发射海丝一号C波段海洋观测卫星可获取米级分辨率SAR影像，将致力于提供高分辨率海洋环境监测服务[40]。
3.4 灾害监测SAR的全天时、全天候特性及其对水体和三维结构的敏感性使它在灾害监测中具有独特优势，有助于形成对地震及次生灾害、山体滑坡及泥石流、森林火灾、台风及洪涝等自然灾害的应急快速反应能力。SAR数据被用于2016年意大利阿马特里斯地震[130]、2017年四川滑坡灾害[131]、2019年加拿大森林火灾[132]、2020年鄱阳湖洪水[133]、2022年青海地震[134]、2022年汤加火山喷发[135]等大型灾害的应急监测中，为有关事件的救援、时空衍化追溯、灾情发展预测等工作提供了宝贵的信息。特别地，干涉SAR的三维信息获取能力对于地质灾害的预测和分析具有非常重要的意义，可提高有关人员提前疏散的成功率，有利于减轻生命和财产损失。
4 总结与展望新型SAR系统通过多种技术手段实现了大幅宽观测、高分辨率观测、多角度观测、多维度观测、多模式观测等更全面、深入的对地探测能力，通过更先进的信息提取技术，可用来获取大范围、多时相、多层次、多角度的集成对地观测数据。随着数据的增多、数据使用成本下降、数据获取机动性和灵活性的提高，以及国产系统的蓬勃发展，新型SAR将在城市管理、植被调查、极地和海洋测绘、灾害监测等领域中发挥更为重要的作用，作为光学数据时空覆盖和探测维度的扩展，也在全面提升对地观测系统的综合信息获取能力。
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