Speckle filtering of Synthetic Aperture Radar images using filters with object-size-adapted windows

Speckle degrades the radiometric quality of a Synthetic Aperture Radar (SAR) image. Previous methods for speckle reduction have used a fixed-size window for filtering the entire image. This, however, may not be effective for the entire image, as land covers of different sizes require different filtering windows. In this paper, a novel method is proposed by which each pixel in the image is filtered with a window appropriate for the size of object within it. The real in-phase and the imaginary quadrature components of the SAR images determine the best window size and the pixels in the intensity image are filtered using their own optimal windows. The proposed method is presented for both single- and multi-polarized SAR images, and the results of several common filters that were modified are presented. This approach is applied to two RADARSAT-2 images: one over San Francisco, California, USA and the other over St. John’s, Newfoundland and Labrador, Canada, producing results that were similar to, or outperformed, comparable filters while retaining details and suppressing speckle effectively. While the method was successful for single-look intensity data, it offers great potential for multi-look and amplitude data as well.     

PolSAR相干斑抑制中的非局部均值滤波研究进展

为促进非局部均值滤波在PolSAR相干斑抑制技术领域的应用与发展,首先简要阐述了非局部均值滤波的基本理论;然后系统梳理了非局部均值在PolSAR相干斑抑制处理中应用的历史沿革,最后深入探讨了现有研究工作的不足之处及其解决之道,为该方向进一步开展研究工作提供有益参考。     

星载合成孔径雷达回波数据仿真研究

本文对星载合成孔径雷达（SAR）图像仿真方法进行了研究,在分析并建立合成孔径雷达回波信号数学模型的基础上,提出了基于分布目标的SAR回波信号数据仿真,进而提出了一种与传统实现方法不同的SAR原始回波数据的生成方法,并用这种方法进行了初步的建模和仿真,验证了其有效性。     

Investigating the correlation between radar backscatter and in situ soil property measurements

Utilizing remote sensing techniques to extract soil properties can facilitate several engineering applications for large-scale monitoring and modeling purposes such as earthen levees monitoring, landslide mapping, and off-road mobility modeling. This study presents results of statistical analyses to investigate potential correlations between multiple polarization radar backscatter and various physical soil properties. The study was conducted on an approximately 3 km long section of earthen levees along the lower Mississippi river as part of the development of remote levee monitoring methods. Polarimetric synthetic aperture radar imagery from UAVSAR was used along with an extensive set of in situ soil properties. The following properties were analyzed from the top 30–50 cm of soil: texture (sand and clay fraction), penetration resistance (sleeve friction and cone tip resistance), saturated hydraulic conductivity, field capacity, permanent wilting point, and porosity. The results showed some correlation between the cross-polarized (HV) radar backscatter coefficients and most of these properties. A few soil properties, like clay fraction, showed similar but weaker correlations with the co-polarized channels (HH and VV). The correlations between the soil properties and radar backscatter were analyzed separately for the river side and land side of the levee. It was found that the magnitude and direction of the correlation for most of the soil properties noticeably differed between the river and the land sides. The findings of this study can be a good starting point for scattering modelers in a pursuit of better models for radar scattering at cross polarizations which would include more diverse set of soil parameters.     

SAR点目标的响应形状

人们普遍认为,SAR点目标响应是sinc函数形状,但是,在通常条件下,SAR点目标看上去不象sinc函数形状。文章从驻留相位原理的近似性,数字信号处理和连续信号处理的差别,人类视觉,检波造成的频谱扩展,SAR的斜视等方面,指出其原因,并阐述了使点目标形状象sinc函数形状需要满足的条件。并提出SAR图像从复数图到模值图的转化过程需要满足的条件和处理。     

合成孔径雷达与森林地上生物量反演：好奇和实用的平衡

好奇是科学与技术发展、不断创新的源泉,实用则是科技发展、创新的结果服务人类、回馈社会,两者无不例外共同不断地驱动着合成孔径雷达技术和森林地上生物量反演的研究和发展。在全球气候急剧变化的今天,好奇和实用的平衡则更加至关重要。地球陆地表面森林面积大小和树木生物量是估算各国或者全球碳储量、排放及其变化的重要参数。好奇已产生的成功案例表明,树木生物量的反演与全极化、极化干涉、层析合成孔径雷达SAR(Synthetic Aperture Radar)技术及数据集紧密相关。反思过去,兼顾好奇和实用,对地上树木生物量的反演,建议将来考虑以下4种方案,即星载双波段SAR和异速方程(Allometric Equation),星载激光雷达LiDAR(Light Detection and Ranging)和异速方程,第3个方案是前面两个方案的结合,方案4为探索和好奇性的。     

Maximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas

The reconstruction of digital surface models (DSMs) of urban areas from interferometric synthetic aperture radar (SAR) data is a challenging task. In particular the SAR inherent layover and shadowing effects need to be coped with by sophisticated processing strategies. In this paper, a maximum-likelihood estimation procedure for the reconstruction of DSMs from multi-aspect multi-baseline InSAR imagery is proposed. In this framework, redundant as well as contradicting observations are exploited in a statistically optimal way. The presented method, which is especially suited for single-pass SAR interferometers, is examined using test data consisting of experimental airborne millimeterwave SAR imagery. The achievable accuracy is evaluated by comparison to LiDAR-derived reference data. It is shown that the proposed estimation procedure performs better than a comparable non-statistical reconstruction method.     

Radargrammetric registration of airborne multi-aspect SAR data of urban areas

In this paper, the registration of decimeter-resolution airborne multi-aspect SAR (MASAR) data of inner city areas by application of the radargrammetric range-Doppler equations is investigated. The geometrical model is adapted to linear flight trajectories and zero-Doppler processed SAR data, whereas the observed trajectory parameters are adjusted using a strict Gauss–Helmert model and known ground control points. The significance of the estimated corrections is examined and the most suitable set of free parameters is determined. Finally, the methodology is applied to real test data of an airborne campaign over the city of Munich, Germany, and the feasability of the proposed radargrammetric registration method is shown.     

星载SAR影像解译研究——以横断山脉地区为例

雷达遥感具有全天时、全天候的特点,在测绘中能够很好地弥补光学遥感的不足。随着SAR技术的不断发展,雷达影像得到了广泛的应用。然而受SAR相干侧视成像机制的影响,山区SAR影像的判读解译依然面临着巨大的挑战,制约着SAR在高山区测图、土地利用、地表覆盖等制图中的应用。本文以横断山脉地区为例,综合利用TerraSAR高分辨率升降轨影像,运用影像融合和镶嵌技术,结合实地调绘成果,对山区高分辨率星载SAR影像判读解译进行了深入研究,并最终形成了一套高效、可靠的解译流程。该判读解译流程对山区SAR影像在高山区测图、土地利用、土地覆盖制图中应用的推广具有重要的意义。     

极化SAR影像弱散射地物统计分类

针对Wishart分类器对功率具有较强的依赖性, 不易区分极化SAR影像上水体、道路、裸土、阴影等弱后向散射地物的问题, 提出一种利用极化目标分解和假设检验的弱散射地物统计分类方法。即在H-α初始化的基础上, 使用似然比检验得出像元与每个类中心的相似性, 并将其作为像元与类中心的距离测度。根据第一类错误概率和统计量的概率分布, 将相似性很小的强散射点归为拒绝类, 减少对分类的影响;对不能显著拒绝的像元归入具有最小统计量的类别中。通过使用E-SAR L波段和Radarsat-2 C波段全极化数据进行实验, 结果表明本文方法有利于弱散射地物极化信息的利用, 能够实现水体、道路、裸露的土壤和阴影等的精确分类。     

随机介质遮蔽条件下的雷达成像方法效能比较

为了综合分析受随机介质遮蔽目标雷达成像方法效能,本文系统比较了随机介质参数(散射厚度与吸收厚度)、天线布局方式(稀疏阵列的天线阵元间距及空间分布)对合成孔径雷达SAR、时间反转TR以及时间反转—多重信号分类TR-MUSIC这3种典型方法成像效能的影响.结果表明TR-MUSIC表现最优,SAR次之,TR较弱.随着光学厚度...     

栈式稀疏自编码网络的多时相全极化SAR散射特征降维

利用极化合成孔径雷达（PolSAR）能够实现地物的识别和分类,而多时相全极化SAR可以获取地物更多的散射特征,提升地物识别精度,但高维散射特征的引入会带来严重的维数灾难问题。为了实现对高维散射特征的有效降维,本文提出一种基于栈式稀疏自编码网络S-SAE（Stacked Sparse AutoEncoder）的多时相PolSAR散射特征降维方法。该方法首先对PolSAR数据进行极化目标分解以获取高维散射特征;然后使用S-SAE对获取的多维特征进行降维处理,其中S-SAE降维方法首先采用无监督训练方式进行逐层贪婪训练;再结合Sigmod分类器,利用监督训练的方式对S-SAE进行参数优化,实现高维特征的有效降维;最后以降维后的特征作为支持向量机（SVM）和卷积神经网络（CNN）分类器的输入,实现地物分类。通过仿真和实测的两组多时相Sentinel-1数据处理结果表明,双隐层的S-SAE降维方法在各分类器上均取得最优的降维效果;对比各降维方法在SVM分类器上的分类精度,S-SAE较于局部线性嵌入（LLE）与主成分分析（PCA）降维方法,总体分类精度分别至少提升了9%和14%;在CNN分类器上,S-SAE较于LLE与PCA降维方法,总体分类精度分别至少提升了7%和9%。     

运动干扰站的SAR-GMTI余弦调相转发干扰

针对多通道合成孔径雷达地面动目标显示SAR—GMTI提出一种基于运动干扰站的余弦调相转发干扰方法。该方法利用运动干扰站转发式干扰的成像特性实现方位向扩展,利用余弦调相信号特性实现距离向扩展,将二者有机结合,可同时实现干扰目标在方位向和距离向的2维扩展。以三通道干涉对消技术为例分析了其对GMTI的对抗性能,理论分析和仿真实验表明:该方法对SAR和SAR-GMTI均可产生灵活可控的2维条带状或面状遮盖干扰效果,但由于多通道GMTI对干扰的抑制和对消,干扰目标幅度受正弦调制系数影响将出现增强或削弱。     

不同TEC分布模式对星载SAR立体定位精度的影响及校正

基于SAR成像模型和R-D定位模型,分析了电离层对星载SAR立体定位精度的影响,提出了相应的校正方法,并进行了仿真实验。实验表明对于特定波长的SAR信号,不同TEC分布模式组合对SAR立体像对定位精度的影响不同,校正效果也不同:当两幅影像对应的TEC分布均匀且大小相同时,电离层对SAR立体定位精度的影响小到可以忽略;当两幅影像由于拍摄季节不同导致TEC不同时,电离层影响很大,特别是距离向精度,其影响程度与两幅影像对应的TEC差值正相关,此时定向参数精化法校正效果很好;当电离层发生扰动时,电离层影响随着扰动程度的增大而增大,定向参数精化法有一定效果,但当电离层扰动很大时,校正效果明显减弱,还需要通过斜距直接校正法等其他方法予以校正。     

双通道SAR振动目标快速检测

目标的振动会对雷达回波产生特殊的相位调制,称为微多普勒效应,能够提供对微动目标检测的有利信息,因此对提高SAR(Synthetic Aperture Radar)系统性能具有重要意义。然而,已有的检测算法存在运算量大、抗杂波噪声能力弱和无法适应多振动目标等问题。针对这些问题,本文提出一种对振动目标检测的新算法。该算法利用相位中心天线偏置DPCA(Displaced Phase Center Antenna )对消技术消除杂波,并沿方位向累加DPCA信号来提高算法的抗噪声能力。由于振动目标SAR方位回波的频谱与脉冲序列具有高度相似性,本文算法选择了检测重复脉冲序列的脉冲重复频率PRI(Pulse Repetition Interval )变换法来实现振动目标的检测。仿真实验表明,本文算法能够在强杂波噪声条件下检测振动目标,同时具有准确振动频率估计性能,甚至当同一个单元存在多个振动目标时,本文算法依然适用。仿真中振动目标检测的计算机运行总时间不超过0.6 s,说明本文算法适用于实时检测,通过与GLRT算法和Hough变换算法运算量的比较,证明了本文算法相比于经典算法具有运算量小,检测速度快的优点。     

SARBuD 1.0： 面向深度学习的GF-3 精细模式SAR建筑数据集

合成孔径雷达SAR（Synthetic Aperture Radar）是开展城市建筑区信息获取与动态监测的重要数据源。本文建立了一个面向深度学习建筑区提取的中高分辨率SAR建筑区数据集SARBuD1.0 （SAR BUilding Dataset）。该数据集包含了覆盖中国不同区域的27景高分三号（GF-3）精细模式SAR图像,并从中获取了建筑区共计60000个SAR样本数据,结合光学图像与专家解译,制作了与样本数据对应的标签图像。SARBuD1.0数据集包含了不同地形场景类型、不同分布类型、不同区域的建筑区。该数据集可支持研究者对建筑区进行图像特征分析、辅助图像理解,并可对当前热点深度学习方法提供训练、测试数据支持。本文以山区建筑为例,使用传统纹理特征与深度学习特征对建筑区进行了特征分析与比较,相比于传统的人工设计的纹理特征,卷积神经网络具有更深、更多的特征,利用网络模型浅层的不同卷积核采样可得到各种纹理特征,在网络的深层卷积结构中可获取代表着类别的深层语义特征,使得分类器能更好地检测并提取图像中指定的目标。基于本数据集利用深度学习方法对不同地形区域的建筑区进行提取实验。实验结果表明基于本数据集训练的深度学习模型,对建筑区提取可以取得良好的结果,说明该数据集可以很好支持面向大数据的深度学习方法。其他学者可以基于SARBuD1.0数据集开展建筑区图像特征分析与语义分割提取等方面的研究。     

基于SL-ICA算法的SAR图像混合像元分解

为解决合成孔径雷达(SAR)图像存在大量混合像元的问题,针对传统ICA不能有效解决混合像元分解这一缺陷,提出一种新的独立成分分析算法--有监督学习ICA算法(SL-ICA).其目标函数是在原ICA负熵目标函数基础上增加监督学习的约束条件项,进而在同一目标函数内实现负熵和约束条件的统一,在最大化负熵的同时也最小化了约束条件的误差,此外,采用一种新的双梯度下降法优化迭代,提高计算速度.并以人工模拟SAR图像和北京地区ENVISAT-ASAR作为数据源进行实验,实验结果明显优于主成分分析方法(PCA)的分解结果.     

基于时空域密度异常的土地利用/土地覆盖短期变化检测

论文分析了时间序列遥感影像中土地利用/土地覆盖短期变化的特点及其时空异常特征, 认为和环境、物候等因素造成的影像变化相比, 由人为活动引起的土地利用/土地覆盖变化具有典型的时间和空间异常特征, 并提出了基于密度异常的土地利用短期变化检测方法。研究工作选取珠江口地区1—5月作物生长期间的3个时间序列Radarsat雷达影像进行试验, 在影像分割的基础上, 构建了基于对象的特征变化矢量, 并将密度异常检测算法(DBAD)扩展到变化矢量的N维特征空间上, 运用随机搜索策略确定检测参数, 对Radarsat时间序列变化矢量中的“小模式”事件进行了检测。检测结果认为, 密度异常检测算法检测的是变化矢量在特征空间的密度分布, 与变化矢量的强度和方向无关, 因此能在时间序列影像中分离出由典型的、正常的作物生长或农事活动引起的影像光谱或回波变化, 进而识别出由人为活动或突发事件导致的土地利用/土地覆盖变化, 这是通常的图像差值等方法难以做到的。进一步的抽样检测说明, 密度异常检测方法对新增建设用地的检测准确率最高(>88%);林地地表覆盖相对稳定, 检测误差也很低(8%);农用地和养殖水面的异常变化检测误差在11%—22%之间;较大的检测误差主要集中在建设用地、农用地和未利用地之间的转换(16%—25%);此外, 养殖水面的检测误差主要集中在河流沿岸及水面变化较大的养殖区域。影像分割结果特别是一些线状分割图斑以及混合地类图斑对误差也有一定的影响。     

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per- formance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91％ for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05％ using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.