利用星载ERS-2 SAR进行长江口海面风场反演研究

基于SAR图像反演原理,提出一种后向散射系数反演海面风场的新方法,以相关长度确定风向,再利用随机粗糙海面理论模型反演风速.并以ERS-2 SAR为数据源,以长江口海区为研究区,获取了海面风场，反演结果和浮标测量风场对比,相符得很好,证明了该方法的可行性。     

基于Gabor小波变换的机载SAR海面风向反演方法

给出了一种基于Gabor小波变换的机载合成孔径雷达（SAR）海面风向反演的新方法。该方法利用Gabor小波对SAR图像进行二次小波分解,并对小波系数作FFT变换来获取图像谱,其低波数谱连线的垂线方向就是海面风场的风向。利用该方法获得了SAR图像海面风向信息,并与àtrous算法反演结果、浮标测得的海面风向（真值）进行了比较。实验结果表明,采用该方法获得的机载SAR海面风向反演结果与海面浮标实测数据吻合,比àtrous算法有较大改进。     

一种基于灰度互相关法的扫描模式下的风场反演算法

该文分析了传统风场反演算法的理论依据、特点和不足,提出一种新的扫描模式下的风场反演算法。该算法考虑两幅相邻扫描周期所成合成孔径雷达图像中海浪波纹的相关特性,应用互相关方法,求解波纹运动矢量,确定风场风向,然后代入地球物理模型求解风速。与传统风场反演算法及浮标实测数据对比可知,该算法提高了风场反演的精确度,且不存在风向模糊问题。机载雷达实测数据的处理结果证明了方法的有效性。     

高频地波雷达风速直接反演的经验模型

利用风浪经验模型从高频地波雷达(HFSWR)的回波谱数据中反演风速需要有效波高等先验信息,因此风速的反演受有效波高反演精度的影响。该文基于风浪经验模型,利用风速和高频地波雷达海面回波二阶谱与一阶谱能量之比的关系,发展了无需波高信息的风速直接反演的经验模型。将风速反演经验模型应用到高频地波雷达风速的反演中,对两部不同频率的雷达在不同海域获得的数据进行了比较分析,结果表明,该文中采用的经验模型能够有效地对风速进行反演,其中三参数模型的结果略好于双参数模型。     

相干多普勒激光雷达风场反演方法研究与实验印证

提出基于共轭梯度算法的速度方位显示(VAD)风场反演方法,应用最优化理论,将共轭梯度算法代替传统VAD方法中的傅里叶级数展开来求取最优解,并针对算法在风场反演应用时存在不收敛于最优解的问题,使用Hessian矩阵对算法进行了修正。同时开展了多普勒激光雷达与符合IEC 61400-12-1国际标准的高精度风杯风速计的43天对比实验,结果显示,当激光雷达的方位角扫描范围为60°、径向个数为7个时,两者的风速、风向相关系数分别为0.991和0.998,风速、风向标准偏差分别为0.52m/s和5.1°,风速、风向偏差分别为-0.02m/s和3.6°。对比实验结果表明,基于共轭梯度算法的VAD风场反演方法使用较小的扫描方位角仍能保证其测量的准确性满足国际标准,具有更强的适用性,同时印证了激光雷达系统的测量性能,为动态复杂风场的监测提供了更佳的选择。     

利用浮标和NWP风场对HY-2散射计联合定标验证

本文对海洋二号卫星微波散射计(Haiyang-2 Scatterometer,HY-2 SCAT)进行了海洋定标算法研究,并使用数值天气预报模型风场(Numerical Weather Prediction,NWP)和浮标数据对定标后反演风场进行联合验证.通过匹配2012年12月份的HY-2 SCAT反演风场、NWP风场及浮标的观测数据,共得到无降雨条件下的3112个25km分辨率的匹配数据.对匹配数据进行分析时,采用基于变量的误差分析方法能够得到比传统线性回归方法更精确的验证结果.选取在风场U、V分量进行联合验证能得到较在风速、风向上更为有利的验证结果.验证结果表明,经过海洋定标法之后的HY-2 SCAT测量后向散射系数的误差残余小于0.15dB,其反演风场与浮标及NWP数据相吻合,U、V分量相对浮标及NWP数据偏差均小于0.23m/s,验证了该定标算法的有效性及定标后反演风场的高精度.     

一种快速有效的SAR图像起伏地形分割算法

根据合成孔径雷达(SAR)成像机理和图像特征,针对SAR图像中的起伏地形,提出了一种基于有向多尺度模型分割算法。该方法利用了SAR图像不同方向不同尺度间的统计相依性,而且考虑了SAR图像的空间信息。由于是基于有向多尺度模型少量的特征数据,利用最大期望(EM)算法可以快速估计参数,同时利用数学形态学算法进行分割修正,实现了高精度的SAR图像起伏地形的快速分割。实测SAR图像分割实验结果证明,对比其他分割算法,该方法对起伏地形的分割效果更为精确。     

基于极化干涉SAR 反演植被高度的改进三阶段算法

利用极化干涉合成孔径雷达(Polarimetric Interferometry SAR, PolInSAR)数据反演森林参数问题为当前PolInSAR 研究的热点问题。经典的森林参数反演算法是基于随机散射体模型(Random Volume over Ground, RVoG)的阶段反演算法,该算法中直线拟合误差和体散射估计误差会严重影响反演精度。为了提高树高估计精度,该文使用整体最小二乘法直线拟合得到更精确的地表相位估计结果,并提出以Gamma 函数为线性度量自适应地估计体散射去相干,得到了改进的PolInSAR 三阶段反演算法,实验结果表明改进算法可靠有效。      

三通道瑞利散射测风激光雷达风速反演算法

为了获得更为准确的大气风速廓线,使用国内首台基于三通道Fabry-Perot标准具的瑞利散射测风激光雷达进行风场测量,提出了根据实测数据及时调整标准具位置的激光频率反演方法。此方法有效减小了激光频率漂移和抖动造成的反演误差,同时对传统风速算法进行了改进。利用非线性迭代算法处理数据,通过对两种算法处理结果的比较,发现迭代算法具有更高的反演精度。使用非线性迭代算法对三通道瑞利散射多普勒测风激光雷达风场测量数据进行处理,得到了10km～40km的大气风速廓线。结果表明,此种风速反演算法切实可行,并提高了反演精度。     

基于模型的单幅高分辨SAR图像建筑物高度反演方法

高分辨合成孔径雷达(SAR)图像的丰富使得建筑物三维信息获取成为城区遥感的一项重要研究课题.针对单幅SAR图像建筑物的高度估计问题,提出了一种基于模型的高度反演方法.该方法遵循假设检验的思路,通过正交投影模型实现模型假设的快速生成,并设计了基于分割的似然函数,利用模拟退火算法在高维模型假设空间中搜索最优解.基于模拟和实测机载SAR图像的实验验证了算法的有效性.     

An L-band geophysical model function for SAR wind retrieval using JERS-1 SAR

An L-band geophysical model function is developed using Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) data. First, we estimate the SAR system noise, which has been a serious problem peculiar to the JERS-1 SAR. It is found that the system noise has a feature common in all the SAR images and that the azimuth-averaged profile of noise can be expressed as a parabolic function of range. By subtracting the estimated noise from the SAR images, we can extract the relatively calibrated ocean signals. Second, using the noise-removed SAR data and wind vector data from the NASA Scatterometer and buoys operated by the Japan Meteorological Agency, we generate a match-up dataset, which consists of the SAR sigma-0, the incidence angle, the surface wind speed, and wind direction. Third, we investigate the sigma-0 dependence on incidence angle, wind speed, and wind direction. While the incidence angle dependence is negligible in the present results, we can derive distinct sigma-0 dependence on wind speed and direction. For wind speeds below 8 m/s, the wind direction dependence is not significant. However, for higher wind speeds, the upwind-downwind asymmetry becomes very large. Finally, taking into account these characteristics, a new L-band-HH geophysical model function is produced for the SAR wind retrieval using a third-order harmonics formula. Resultant estimates of SAR-derived wind speed have an rms error of 2.09 m/s with a negligible bias against the truth wind speed. This result enables us to convert JERS-1 SAR images into the reliable wind-speed maps.     

Validating a scatterometer wind algorithm for ERS-1 SAR

The ocean surface wind field is observed from space operationally using scatterometry. The European Space Agency's (ESAs) ERS-1 satellite scatterometer routinely produces a wind product that is assimilated into forecast models. Scatterometry cannot give accurate wind estimates close to land, however, because the field of view of a spaceborne scatterometer is on the order of 50 km. Side lobe contamination, due to the large contrast in backscatter between land and water, compounds the problem. Synthetic aperture radar (SAR) can provide wind speed and direction estimates on a finer scale, so that high-resolution wind fields can be constructed near shore. An algorithm has been developed that uses the spectral expression of wind in SAR imagery to estimate wind direction and calibrated backscatter to estimate wind strength. Three versions, based on C-band scatterometer algorithms, are evaluated for accuracy in potential operational use. Algorithm estimates are compared with wind measurements from buoys in the Gulf of Alaska, Bering Strait, and off the Pacific Northwest coast by using a data set of 61 near-coincident buoy and ERS-1 SAR observations. Representative figures for the accuracy of the algorithm are ±2 m/s for wind speed and ±37° for wind direction at a 25-km spatial resolution     

一种用于海洋要素反演的机载SAR多普勒中心偏移计算方法

多普勒中心偏移是合成孔径雷达(SAR)反演海面风场、海表流场的重要参数。该文针对机载正侧视提出一种多普勒中心偏移计算方法,分别利用载机运动状态数据和海洋探测回波数据计算多普勒中心频率,再作差求解多普勒中心偏移,并在多普勒谱分析中加入小波分析去除噪声的影响来提高计算精度。以CDOP经验模型计算结果作为比对真值,机载SAR飞行探测试验结果表明,9组探测数据多普勒中心偏移计算误差的绝对值均小于2 Hz,均方根误差为1.4 Hz,满足海洋环境要素反演的精度要求。实验表明高精度的平台运动状态数据和探测回波数据是多普勒中心偏移海洋应用的关键。      

双激光雷达的水平风场估计方法

针对民航机场区域内单部激光雷达探测水平风场存在较大误差的问题,提出一种基于支持向量回归的双激光雷达水平风场估计模型。该模型以两部激光雷达重叠扫描区域风速为基础,对非重叠区雷达径向的其他数据点水平风速进行估计。首先,提取重叠区的径向风速、水平风速和距离三个特征,以重叠区域数据点为训练集,在同一维度规范化后设定惩罚因子和核函数参数,用支持向量回归得到初始估计值。然后,以单部激光雷达的径向风速为先验条件,估计出非重叠区相邻径向点水平风速。将估计的结果扩展为新的训练集,依次逐步扩大训练集进而估计出非重叠区的水平风速。最后,通过实测数据分析了该方法逐步估计的误差,分析了风速大小和回波信噪比对该方法估计性能的影响,结果表明该方法估计的风场的均方根误差较单部雷达的均方根误差更小,减小了水平风速误差,扩大了双激光雷达探测水平风场范围,提高了雷达的利用率。     

A new wind vector algorithm for C-band SAR

Ocean wind speed and wind direction are estimated simultaneously using the normalized radar cross sections /spl sigma//sup 0/ corresponding to two neighboring (25-km) blocks, within a given synthetic aperture radar (SAR) image, having slightly different incidence angles. This method is motivated by the methodology used for scatterometer data. The wind direction ambiguity is removed by using the direction closest to that given by a buoy or some other source of information. We demonstrate this method with 11 ENVISAT Advanced SAR sensor images of the Gulf of Mexico and coastal waters of the North Atlantic. Estimated wind vectors are compared with wind measurements from buoys and scatterometer data. We show that this method can surpass other methods in some cases, even those with insufficient visible wind-induced streaks in the SAR images, to extract wind vectors.     

Wind vector retrieval using ERS-1 synthetic aperture radar imagery

An automated algorithm intended for operational use is developed and tested for estimating wind speed and direction using ERS-1 SAR imagery. The wind direction comes from the orientation of low frequency, linear signatures in the SAR imagery that the authors believe are manifestations of roll vortices within the planetary boundary layer. The wind direction thus has inherently a 180° ambiguity since only a single SAR image is used. Wind speed is estimated by using a new algorithm that utilizes both the estimated wind direction and σ ₀ values to invert radar cross section models. The authors show that: 1) on average the direction of the roll vortices signatures is approximately 11° to the right of the surface wind direction and can be used to estimate the surface wind direction to within ±19° and 2) utilizing these estimated wind directions from the SAR imagery subsequently improves wind speed estimation, generating errors of approximately ±1.2 m/s, for ERS-1 SAR data collected during the Norwegian Continental Shelf Experiment in 1991     

星载合成孔径雷达遥感海洋风场波浪场

星载合成孔径雷达(SAR)能够全天时、全天候、高空间分辨率、宽刈幅观测海洋表面，是获取海面风场和波浪场信息的重要微波传感器。该文综述了多极化SAR海面风场遥感原理、地球物理模式函数，以及潜在应用(海气边界层现象、海上风能资源开发、台风监测与预警预报)，系统总结了传统星载SAR、新型干涉和极化SAR海浪遥感方法和技术。随着雷达卫星编队飞行技术的逐步成熟，未来海洋卫星组网将成为全球海洋和极地观测新趋势，合成孔径雷达海面风场和波浪场定量遥感将从科学研究向业务化海洋动力环境监测发展。      

基于并联卷积神经网络的SAR图像目标识别

基于合成孔径雷达（synthetic aperture radar，SAR）在图像目标识别领域中识别精度低的问题，设计一种利用并联卷积神经网络（convolutional neural network，CNN）来提取SAR图像特征的目标识别方法.首先利用改进的ELU激活函数代替常规的ReLU激活函数，建立与二次代价函数相结合的深度学习模型.其次采用均方根支柱（root mean square Prop，RMSProp）与Nesterov动量结合的优化算法执行代价函数参数迭代更新的任务，利用Nesterov引入动量改变梯度，从两方面改进更新方式，有效地提高网络的收敛速度与精度.通过对美国国防研究规划局（DARPA）和空军研究实验室（AFRL）共同推出的MSTAR数据集进行实验，实验表明，该文提出的算法能充分提取出SAR图像中各类目标所蕴含的信息，具有较好的识别性能，是一种有效的目标识别算法.     

MTPT 方法估计机载SAR 残余运动的两个影响因素

由于导航系统测量精度的限制,载机的位置经常存在厘米级的误差,该误差称为残余运动误差。对于机载超高分辨SAR 系统或机载重轨干涉SAR,必须估计并补偿该残余运动误差。MTPT 方法可以估计单幅SAR 图像中的残余运动误差,但是速度和斜距的误差会影响该方法的精度。该文在详细分析速度和斜距误差对MTPT 方法进行残余运动估计的影响的基础上,利用仿真和实测SAR 数据验证了这一点。同时还指出,MTPT 方法虽然可以估计速度和斜距误差,但是它们的精度敏感于相位测量误差;在利用MTPT 方法进行估计之前必须先利用其它更为准确的方法消除平台的速度误差和目标的斜距误差。      

Residual SAR focusing: an application to coherence improvement

The focusing quality of a SAR processor greatly depends on the accuracy of the system geometry estimate. Sometimes ancillary data do not provide enough accuracy, therefore autofocusing has to be performed to get the finest quality possible. A “residual” azimuth compression is introduced to show how a defocused image can be compensated by means of a monodimensional local operator. The residual transfer function that generates defocusing is then derived. The effects of the defocusing are shown on both a complex single SAR image and a SAR interferogram. SAR interferograms, however, are much more sensitive to defocusing than the single SAR image. Two algorithms have been developed to estimate, and compensate for, the defocusing in both the single SAR image and SAR interferometric cases. The processors select data suitable for estimating focusing parameters from the whole images by exploring Kurtosis (for single image focusing) or coherence (for interferometric autofocusing). The residual, short time-domain operator is then exploited to retrieve the focusing parameter values and, finally, to get the focused image. The limitations and accuracy of the algorithm in terms of parameter estimation are investigated. Experimental results, obtained from different SAR missions, are presented