Shallow sea topography detection using fully Polarimetric Gaofen-3 SAR data based on swell patterns

Compared to single-polarization synthetic aperture radar(SAR)data,fully polarimetric SAR data can provide more detailed information of the sea surface,which is important for applications such as shallow sea topography detection.The Gaofen-3 satellite provides abundant polarimetric SAR data for ocean research.In this paper,a shallow sea topography detection method was proposed based on fully polarimetric Gaofen-3 SAR data.This method considers swell patterns and only requires SAR data and little prior knowledge of the water depth to detect shallow sea topography.Wave tracking was performed based on preprocessed fully polarimetric SAR data,and the water depth was then calculated considering the wave parameters and the linear dispersion relationships.In this paper,four study areas were selected for experiments,and the experimental results indicated that the polarimetric scattering parameterαhad higher detection accuracy than quad-polarization images.The mean relative errors were 14.52%,10.30%,12.56%,and 12.90%,respectively,in the four study areas.In addition,this paper also analyzed the detection ability of this model for different topographies,and the experiments revealed that the topography could be well recognized when the topography gradient is small,the topography gradient direction is close to the wave propagation direction,and the isobath line is regular.

Evaluation of Wind Retrieval from Co-Polarization Gaofen-3 SAR Imagery Around China Seas

Gaofen-3(GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar(SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been operational since January 2017. In this study, we have collected more than 1000 quad-polarization(vertical-vertical(VV); horizontal-horizontal(HH); vertical-horizontal(VH); horizontal-vertical(HV)) GF-3 SAR images, which were acquired around the China Seas from September 2016 to September 2017. Wind streaks were visible in these images in co-polarization(VV and HH) channel. Geophysical model functions(GMFs), including the CMOD5N together with polarization ratio(PR) model and C-SARMOD, were used to retrieve winds from the collected co-polarization GF-3 SAR images. Wind directions were directly obtained from GF-3 SAR images. Then, the SAR-derived wind speeds were compared with the measurements at a 0.25? grid from the Advanced Scatterometer on board the Metop-A/B and microwave radiometer WindSAT. Based on the analysis, empirical corrections are proposed to improve the performance of the two GMFs. Results of this study show that the standard deviation of wind speed is 1.63 m s^(-1) with a 0.19 m s^(-1) bias and 1.71 m s^(-1) with a 0.26 m s^(-1) bias for VV-and HH-polarization GF-3 SAR, respectively. Our work not only systematically evaluates wind retrieval by using the two advanced GMFs and PR models but also proposes empirical corrections to improve the accuracy of wind retrievals from GF-3 SAR images around the China Seas and thus enhance the accuracy of near real-time operational SAR-derived wind products.

Semi-Empirical Algorithm for Wind Speed Retrieval from Gaofen-3 Quad-Polarization Strip Mode SAR Data

Synthetic aperture radar(SAR)is a suitable tool to obtain reliable wind retrievals with high spatial resolution.The geophysical model function(GMF),which is widely employed for wind speed retrieval from SAR data,describes the relationship between the SAR normalized radar cross-section(NRCS)at the copolarization channel(vertical-vertical and horizontal-horizontal)and a wind vector.SAR-measured NRCS at cross-polarization channels(horizontal-vertical and vertical-horizontal)correlates with wind speed.In this study,a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3(GF-3)SAR data with noise-equivalent sigma zero correction using an empirical function.GF-3 SAR can acquire data in a quad-polarization strip mode,which includes cross-polarization channels.The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts.In particular,the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS.The accuracy of SAR-derived wind speed is around 2.10ms−1 root mean square error,which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration.The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms.This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.

The impact of rain to observed signal from Chinese Gaofen-3 synthetic aperture radar in typhoons

Gaofen-3(GF-3),a Chinese civil synthetic aperture radar(SAR)at C-band,has operated since August 2016.Remarkably,several typhoons have been captured by GF-3 around the China Seas over its last two-year mission.In this study,six images acquired in Global Observation(GLO)and Wide ScanSAR(WSC)modes at verticalvertical(VV)polarization channel are discussed.This work focuses on investigating the observation of rainfall using GF-3 SAR.These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts(ECMWF),significant wave height simulated from the WAVEWATCH-III(WW3)model,sea surface currents from climate forecast system version 2(CFSv2)of the National Centers for Environmental Prediction(NCEP)and rain rate data from the Tropical Rainfall Measuring Mission(TRMM)satellite.Sea surface roughness,was compared with the normalized radar cross section(NRCS)from SAR observations,and indicated a 0.8 correlation(COR).We analyzed the dependences of the difference between model-simulated NRCS and SARmeasured NRCS on the TRMM rain rate and WW3-simulated significant wave height.It was found that the effects of rain on SAR damps the radar signal at incidence angles ranging from 15°to 30°,while it enhances the radar signal at incidence angles ranging from 30°to 45°and incidence angles smaller than 10°.This behavior is consistent with previous studies and an algorithm for rain rate retrieval is anticipated for GF-3 SAR.

Retrieval of Significant Wave Height Under Typhoon Conditions from Gaofen-3 SAR Imagery

The objective of this paper is to propose an empirical method to inverse significant wave height(SWH)under typhoon conditions from collected dual-polarization Gaofen(GF)-3 synthetic aperture radar(SAR)imagery.The typhoon scenes were cap-tured from narrow scan(NSC)and wide scan(WSC)images,and collocated with European Center for Medium-Range Weather Fore-casts reanalysis data of(ECMWF).To improve the quality of GF-3 SAR images,the recalibration over rainforest and de-scalloping were carried out.To establish the empirical relationship between SAR-derived parameters and collocated SWH,the sensitivity analysis of typical parameters about the normalized radar cross section(Nrcs)and imagery variance(Cvar)were performed to both VV and VH polarized images.Four scenes from GF-3 SAR imagery under typhoon conditions were used for training the model by the multivari-ate least square regression,and one scene was used for preliminary validation.It was found that the joint retrieval model based on VV and VH polarized SAR imagery performed better than any single polarized model.These results,verified by using ECMWF data,revealed the soundness of this approach,with a correlation of 0.95,bias of 0 m,RMSE of 0.44 and SI of 0.01 when VV polarization and VH polarization data were both used.

Validation of significant wave height retrieval from co-polarization Chinese Gaofen-3 SAR imagery using an improved algorithm

Chinese Gaofen-3(GF-3) is the first civilian satellite to carry C-band(5.3 GHz) synthetic aperture radar(SAR).During the period of August 2016 to December 2017, 1 523 GF-3 SAR images acquired in quad-polarization(vertical-vertical(VV), horizontal-horizontal(HH), vertical-horizontal(VH), and horizontal-vertical(HV)) mode were recorded, mostly around China's seas. In our previous study, the root mean square error(RMSE) of significant wave height(SWH) was found to be around 0.58 m when compared with retrieval results from a few GF-3 SAR images in co-polarization(VV and HH) with moored measurements by using an empirical algorithm CSAR_WAVE. We collected a number of sub-scenes from these 1 523 images in the co-polarization channel,which were collocated with wind and SWH data from the European Centre for Medium-Range Weather Forecasts(ECMWF) reanalysis field at a 0.125° grid. Through the collected dataset, an improved empirical wave retrieval algorithm for GF-3 SAR in co-polarization was tuned, herein denoted as CSAR_WAVE2. An additional 92 GF-3 SAR images were implemented in order to validate CSAR_WAVE2 against SWH from altimeter Jason-2, showing an about 0.52 m RMSE of SWH for co-polarization GF-3 SAR. Therefore, we conclude that the proposed empirical algorithm has a good performance for wave retrieval from GF-3 SAR images in co-polarization.

Evaluation of wave retrieval for Chinese Gaofen-3 synthetic aperture radar

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3(GF-3)Synthetic Aperture Radar(SAR)images.More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip(QPS)mode.The images had a swath of 30 km and a spatial resolution of 8 m pixel size.They were processed to retrieve Significant Wave Height(SWH),which is simulated from a numerical wave model called Simulating WAves Nearshore(SWAN).The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar(SAR)wave spectrum retrieval.Therefore,we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm(MPI),a Semi-Parametric Retrieval Algorithm(SPRA),and the Parameterized First-guess Spectrum Method(PFSM),in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied.The PFSM can be driven using the wind speed without calculating the dominant wave phase speed.Wind speeds were retrieved using a Vertical-Vertical(VV)polarized geophysical model function C-SARMOD2.The proposed algorithm was implemented for all collected SAR images.A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts(ECMWF)ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error(RMSE).The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE,which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm.

面向目视解译的全极化SAR船只精细化特征表征方法

随着卫星技术的发展,极化合成孔径雷达(PolSAR)数据的分辨率和数据质量得到大幅提升,为人造目标的精细化目视解译提供了良好的数据条件。目前主要采用多分量分解的方法,但是易造成像素错分问题,为此,该文结合Yamaguchi极化分解和极化熵提出了一种非固定阈值划分的方法用于实现全极化SAR图像船只结构精细化特征表征。Yamaguchi极化分解能够识别基本散射机制,其修正后的体散射模型更符合实测数据,可有效对人造目标进行表征。极化熵H在弱去极化状态下可以看成某一指定等效点的目标散射机制,能够有效突出船只主散射特征。因此,该文通过将Yamaguchi极化分解算法的非固定三分量与极化熵的低中高熵内嵌,将其分为非固定阈值的九分类成分,从而降低硬阈值处理在阈值边界处受噪声影响产生的类别随机性。并且将二次散射和单次散射均显著的区域称为混合散射(MSM),以更好匹配实验中船只典型结构的散射类型。在此基础上,利用广义相似性参数进一步缩短类内距离,采用改进后的GSP-Wishart分类器进行迭代聚类,旨在通过提高二次散射和混合散射机制以提高不同类型船只可区分度。最后,该文采用中国上海某港口的高分三号全极化SAR数据进行实验,为了验证每艘船只特征表征正确性,通过船舶自动识别系统(AIS)收集并筛选了该港口船只信息及光学数据,并与极化SAR数据中每艘船只进行匹配。实验结果表明该方法可有效区分散货船、集装箱船和油轮3种类型船只。

GF-3号SAR卫星遥感围填海监测方法研究——以大连金州湾为例

依据不同围填海类型在高分三号(GF-3)合成孔径雷达(synthetic aperture radar,SAR)卫星遥感影像上可分性,建立围填海遥感分类体系及相应的围填海类型解译标志,进而分析SAR图像岸线提取方法,构建GF-3围填海监测技术流程。采用几何主动轮廓模型进行GF-3 SAR影像自动提取海岸线,获得围填海专题图。通过外业精度调查验证GF-3 SAR卫星遥感影像可以有效获取围填海信息。

基于高分三号卫星合成孔径雷达数据的农田土壤水分反演

土壤水分是农作物生长的基本条件,本研究基于高分三号卫星C波段合成孔径雷达数据,提出新的土壤水分反演算法,并获取区域尺度8 m空间分辨率的农田区土壤水分。首先,通过PROSAIL模型、实测植被冠层含水量、Landsat-8光学数据优选光学植被水分指数,计算水云模型参数并获得土壤直接后向散射系数;其次,利用高级积分方程模型模拟雷达后向散射影响机制,采用雷达影像高低入射角特性计算地表组合粗糙度;最后,利用高分三号卫星同极化雷达数据反演农田区土壤水分,并基于实测数据开展精度验证。结果表明:土壤水分反演值与野外实测值具有良好一致性,垂直极化下反演精度更高,其决定系数为0.5956,均方根误差为0.0415 m3/m3。本研究成果可为我国自主研发的高分三号卫星获取高分辨率土壤水分信息提供算法参考。

Estimation of Crop Biomass Using GF-3 Polarization SAR Data Based on Genetic Algorithm Feature Selection

In recent years,Polarization SAR(PolSAR)has been widely used in the filed of crop biomass estimation.However,high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model.Aiming at this problem,we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm(GA).Firstly,the backscattering coefficient,the polarization parameters and texture features were extracted from PolSAR data.Then,these features were automatically pre-selected by GA to obtain the optimal feature subset.Finally,based on this subset,a support vector regression machine(SVR)model was applied to estimate crop biomass.The proposed method was validated using the GaoFen-3(GF-3)QPSΙ(C-band,quad-polarization)SAR data.Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign,the proposed method achieve relative high validation accuracy(over 80%)in both crop types.For further analyzing the improvement of proposed method,validation accuracies of biomass estimation models based on several different feature selection methods were compared.Compared with feature selection based on linear correlation,GA method has increased by 5.77%in wheat biomass estimation and 11.84%in rape biomass estimation.Compared with the method of recursive feature elimination(RFE)selection,the proposed method has improved crops biomass estimation accuracy by 3.90%and 5.21%,respectively.

高分3号星载合成孔径雷达极地海冰自动检测方法研究

随着全球变暖等一系列气候变化的发生,极地海冰成为人们日益关注的焦点。由于不受光线和云雨影响,合成孔径雷达(SAR)可以进行全天时全天候的观测。高分3号是我国高分系列卫星中的一颗星载合成孔径雷达成像卫星,具有多种成像模式,可以在全球获取SAR数据。全天时全天候的工作特性和高空间分辨率的优势,使得高分3号星载SAR在极地海冰遥感监测中发挥重要的作用。本文基于高分3号水平-垂直(Horizontal-Vertical,HV)极化数据,提出了一种基于支持向量机的无需人工干预的海冰检测方法,实现海水和海冰的自动分离。利用该方法得到的海冰和海水分离结果同辅以人工解译的半监督分类结果相比较为吻合,为高分3号服务于极区海冰监测奠定了良好的基础。

一种高分三号SAR影像五参数变换模型几何精校正方法

为了更好且快速的提高单景高分三号影像的地理定位精度。提出了有理函数结合五参数变换方法的复合模型实现高分三号(Gaofen-3,GF-3)卫星一级影像产品的几何精纠正,所提5个参数均具有具体的物理意义。通过比较仅用影像附属的有理多项式参数,以及有理函数模型结合仿射变换方法的定位精度,表明在引入高分辨率数字高程模型且使用本文方法时,只用3~4个控制点便可有效提高GF-3号影像的定位精度。相比仿射变换模型,本文方法具有所需控制点数少且精度高的优点。并利用四景GF-3号合成孔径雷达(synthetic aperture radar,SAR)SAR影像对所提方法进行了实验验证,在引入高分辨率数字高程模型数据后,所提方法可将高分三号超精细条带(ultra fine strip,UFS)模式的影像定位精度纠正到约2个像素,精度要高于仿射变换的方法。

基于SNAP通用架构的高分三号卫星数据转换方法研究

针对目前能够处理国产高分三号卫星数据的软件偏少,以商业闭源为主,授权费用昂贵等一系列问题,提出一种借助免费开源的SNAP遥感处理软件平台,利用其已集成的Sentinel-1工具箱中SAR处理工具集,实现对高分三号卫星数据进行处理的新思路。该思路实现的关键是在SNAP提供的通用架构下构建针对高分三号卫星影像数据的转换工具。通过分析SNAP核心数据数据模型、高分三号卫星数据结构和特点,设计并实现该工具的结构和功能。调用Sentinel-1工具箱对转换后数据进行极化分解和地理编码的处理测试,获得了与实际相符的结果,验证了方法的可行性,为开源技术应用于高分三号卫星数据处理提供了参考。

基于间接校正的高分三号正射影像生成

由于高分三号卫星(gaofen-3 satellite, GF-3)数据问世时间较短、参数不足,现有软件未针对GF-3数据添加传感器支持,导致难以利用现有各类软件生成其正射影像。提出一种基于间接校正的GF-3正射影像生成方法。模拟待校正区域卫星成像过程,采用RD模型利用与该区域对应的DEM数据构建模拟GF-3影像;在模拟与真实GF-3影像中分别提取特征点,并配准提取的特征点对,进而建立两影像间的映射关系;利用模拟GF-3影像分别与DEM数据和真实GF-3影像之间的映射关系,间接完成正射校正,生成正射影像。针对GF-3多成像模式的特点,选取3种具有代表性成像模式的影像进行正射影像生成试验,取得了较好的实验结果。将真实GF-3影像与模拟GF-3影像的对应像元坐标进行比较表明,x和y方向误差均小于一个像元,精度较高。提出算法充分考虑到了GF-3系统的成像特点,在不同的地形细节均取得了较好的结果。定性和定量的分析实验结果验证了提出方法的可行性及有效性。

多角度SAR动目标检测技术及其高分三号实验验证研究

多角度SAR作为一种新的SAR模式,它具备对场景的长时间观测以及大合成孔径角两个优势。已有研究表明,这两点区别于传统SAR模式的优势,使得单通道系统也可以具备较强的动目标检测能力,即,无需增加雷达系统的复杂度,就可以实现在轨星载SAR系统动目标检测能力的拓展和提升。这也使得多角度SAR动目标研究成为新的研究热点。在研讨近年来多角度SAR-GMTI研究基础及发展现状的基础上,该文重点介绍了研究团队围绕高分3号开展的原理性验证实验研究,包括凝视聚束模式动目标检测方法研究、双通道动目实验模式、双通道凝视聚束GMTI模式研究等。通过上述研究,以期为在轨及规划星载SAR单通道GMTI工程实现、未来星载多角度SAR时序动态观测新型工作模式设计等奠定可行性研究基础。

基于多处理板联合SAR成像的分布式计算模拟系统研究

合成孔径雷达(SAR)卫星的探测范围有限,全轨利用率不高,并且单个卫星的计算和存储资源有限,难以完成大量的星载合成孔径雷达数据处理。本文拟采用多处理板联合的数据处理方法,针对星上分布式计算构建一种模拟系统,以提高星上数据处理效率,验证多处理板联合的数据处理方法在高效利用卫星计算与存储资源、提升单个卫星全轨利用率等方面的优点。本系统基于距离多普勒(range Doppler,RD)成像算法的多卫星分布式SAR实时处理方法,并以现场可编程门阵列(FPGA)芯片为核心构建了分布式计算模拟系统。不同于传统的单个卫星RD算法处理过程,该系统将处理过程分为三个阶段。每个阶段内运算任务被合理地分配给不同的数据处理单元。利用高分三号卫星(GF-3)SAR原始数据进行成像处理,以检验方法和系统的性能。

全极化SAR影像多特征分类的海岸线提取方法

快速准确地提取海岸线对研究海岸带和海岸带演变具有重要的意义。针对目前利用SAR影像提取海岸线的精度和自动化程度不能达到一个平衡这一问题,提出了一种结合多特征选择和随机森林分类算法的海岸线提取方法。并以高分三号(GF-3)全极化影像为研究数据,以黄河三角洲地区为研究区,成功提取了黄河三角洲地区的海岸线。该方法结果与OTSU阈值分割方法所提取的海岸线做了对比。结果显示,在40 m分析精度下,该方法海岸线提取结果质量均在90%以上,其提取结果光滑连续且精度可靠,优于直接阈值法提取的结果。

基于高分三号SAR数据的城市建筑高分辨率高维成像

传统合成孔径雷达(SAR)只能获取方位-距离二维图像,无法准确反映目标的三维散射结构信息。层析合成孔径雷达(TomoSAR)是一种多基线干涉测量模式,它将合成孔径原理扩展至高程向,除了可对目标进行二维成像之外,还可以准确恢复目标的高度向散射信息,真正实现三维成像。差分层析合成孔径雷达(DTomoSAR)将合成孔径原理延伸至高程和时间方向,不仅可以获得目标的三维散射结构,还可以高精度获取观测目标的形变速率,实现对目标形变的有效监测。高分三号是我国首颗1 m分辨率C频段多极化SAR卫星。它具有高分辨率、大成像幅宽、多成像模式等特点,对我国高分对地观测技术的发展具有重要意义。目前高分三号数据主要应用于目标识别等图像处理领域,没有充分利用SAR图像的相位信息。而且,由于设计之初未考虑后续高维成像应用,现有高分三号获取的SAR图像存在有一定的空间、时间去相干问题,对应用于后续干涉系列处理产生了一定影响。为解决上述问题,该文基于7景高分三号SAR复图像,开展了对北京雁栖湖周围建筑的三维、四维层析成像研究,在获取了建筑物三维散射结构信息的同时,实现了对建筑物形变的毫米级高精度监测。该初步实验结果证明了高分三号SAR数据的应用潜力,为后续进一步扩展高分三号SAR卫星在城市感知与监测中的应用提供了技术支撑。

高分三号SAR影像L1A级产品精处理方法

L1A级影像产品是中国高分三号SAR卫星产品发布的初级产品形式。为实现后续科学研究、增值应用,提出一套完整的L1A级产品处理流程,用以生成辐射纠正产品、几何纠正产品等高级产品形式。在辐射纠正过程中,为剔除L1A级产品量化过程中存在空值和零值像素对辐射纠正结果造成的统计偏差,基于高分三号等效噪声系数提出改进的辐射纠正方法。在几何纠正中,提出RPC参数反算算法确定影像角点坐标,基于xml元数据文件中提供的轨道方向、视向以及采样间隔参数保证反算方法的稳健性,在几何校正重采样部分引入SAR滤波算子实现等效视数的提升。对使用Envi5.5软件无法完成几何校正的部分高分三号影像,使用本文提出的处理流程可以顺利完成,其处理结果通过与同为C波段的Sentinel-1影像辐射精度,以及Sentinel-2光学影像几何精度的对比,表明本方法对L1A产品进行处理的精确性和可靠性。