三层植被极化干涉建模及参数反演新方法研究

两层植被结构模型是传统三阶段植被参数反演方法的基础,当实际植被结构为三层结构时,三阶段植被参数反演方法精度变差.针对该问题,建立了与三层植被结构模型对应的极化SAR干涉复相关系数模型,分析了传统三阶段植被参数反演方法精度变差的原因,提出了基于双基线极化SAR干涉的三层植被参数反演新方法,新方法充分考虑了三层植被干涉复相...     

The relationship between the backscattering coefficient and thebiomass of narrow and broad leaf crops

The influence of the shape and dimensions of plant constituents on the backscattering of agricultural vegetation is investigated. Multifrequency multitemporal polarimetric data, collected at C- and L-bands by means of airborne and satellite synthetic aperture radar (SAR), showed that the relations between the backscattering of crops and the vegetation biomass depend on plant type, and that there are different trends for “narrow” and “broad” leaf crops. In the latter crops, backscattering increases with an increase in the biomass, especially at L-band. This behavior is typical of media in which scattering is dominant, whereas on “narrow leaf” plants, the trend is flat or decreasing, denoting a major contribution of absorption. Theoretical simulations obtained with a discrete element radiative transfer model have confirmed that a different backscattering of crops with the same biomass may be due to plant geometry     

双基线极化干涉合成孔径雷达的植被参数提取

由于单基线极化干涉SAR技术(如三阶段法、ESPRIT算法等)反演植被参数的方法受限于具有最小地体幅度比的观测数据,影响冠层或地表有效相位中心的估计,因此往往低估植被的高度。该文通过增加另一个基线的极化干涉SAR数据在一定程度上解决单基线反演技术存在的这个缺陷,并适当改进双基线极化干涉SAR技术,降低植被参数估计的动态偏差,最后用模拟数据和实际数据验证了该方法。     

基于有向体模型的植被参数反演及室内宽带实验研究

该文对具有极化特征的植被提出一种基于有向体(OV)模型的参数反演算法,并尝试在微波暗室内构建全极化干涉合成孔径雷达半实物宽带测量系统,对模型及算法进行实验验证。根据OV模型的植被特征,以同极化之差所代表的二面角散射机理作为地形相位点的判别依据,结合计算平均值和标准差的方法反演出植被高度。接下来,在环境可控的微波暗室内构建了实验系统,采用两个高度已知的三面角对干涉基线距进行校准,提高了参数反演精度。实验结果表明:在场景的地体幅度比较大的情况下,植被反演高度与实际高度的误差仅为0.03 m,全频带内的高度反演误差不超过0.2 m,说明了反演算法的有效性。     

Single-baseline polarimetric SAR interferometry

Examines the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR's E-SAR system at L-band     

引入有取向二面角散射的Freeman-Durden 分解

该文首先考察了当极化SAR 方位向与农作物种植行向不一致时,入射电磁波到地表、农作物的二次散射与一般二次散射的区别。其次,为描述这种二次散射,建立了有取向的二面角散射模型,并将该模型引入到Freeman-Durden 目标分解中,设计了相应的目标分解算法。最后,选取同一农作物种植区两种航迹的机载全极化SAR 数据实现了该分解算法。实验结果证明,对于农作物种植区,改进后的Freeman-Durden 分解能提升不同航迹下的极化SAR 数据目标分解的一致性。      

单基线PolInSAR反演算法研究

极化干涉合成孔径雷达(PolInSAR)测量是一种集极化雷达(PolSAR)和干涉雷达(InSAR)测量技术于一体的新的对地观测技术,利用极化干涉雷达数据反演地表植被参数特别是森林的垂直结构参数是当前极化干涉研究的热点问题。经典的单基线PolInSAR反演算法是Cloude和Papathanassiou提出的三阶段反演算法,但是该算法在相干值估计、直线拟合和散射体去相干估计等方面都存在着误差,直接影响反演精度。该文提出了一种新的基于统计特征和PolInSAR相位最优化算法的反演算法,并采用PolInSAR模拟数据验证了该算法的有效性。     

基于分类统计的PolInSAR植被高度最大似然估计

极化干涉SAR是一种集极化和干涉SAR优势于一体的新型遥感技术.结合两层植被随机体散射模型和极化分解技术,基于极化干涉SAR数据的概率分布统计特征,提出一种利用参数迭代求解预测模璎和测量值最小似然距离的植被高度反演方法.该方法克服了传统最大似然估计方法需已知地表散射特征参数的约束,减少了计算复杂性.最后通过极化干涉SAR仿真数据实验分析,文中算法相对于三阶段反演算法提高了植被高度估计的精度.验证了算法的有效性.     

An iterative inversion algorithm with application to thepolarimetric radar response of vegetation canopies

The retrieval of scene parameters from polarimetric radar data using an iterative inversion approach is considered. The theoretical development of a general, model-based iterative algorithm for inversion of polarimetric radar data is presented. Factors relevant to its implementation, such as sensor configuration, algorithm optimization and computational structure are discussed. The algorithm is applied to the specific problem of inverting the vector radiative transfer model for a simplified, representative vegetation canopy consisting of vertical trunks, leaves, and a rough ground surface. The results of this inversion are in excellent agreement with simulated data generated using the radiative transfer model. The convergence properties of the algorithm are evaluated, and it is found that successful convergence is achieved in about 90% to 95% of the cases tested for the implementation used in this work. An error analysis is presented which considers the effect of both systematic and measurement derived errors. Typical error bounds for the current application are approximately ±3%, allowing for ±0.5 dB accuracy in the measured radar data     

具有极化特征的植被相干散射模型及参数反演

重点研究了具有极化特征的植被相干散射模型及参数反演算法。基于目标分解理论,推导了植被及地表回波的极化干涉相干系数与植被高度、衰减系数、地形相位等参数之间的数学关系,以衰减系数作为极化分量的函数,建立了具有明显极化特征的植被及地表相干散射模型。在此基础上,以全极化干涉相干系数作为输入参量,考虑模型对极化的依赖性,采用三阶段法对植被的物理参量进行反演,获得了地形相位和植被高度。在环境可控的微波暗室内构建了极化干涉半实物合成孔径雷达系统对模型进行实验验证,获取了南洋杉和土壤所构成场景的极化干涉回波数据,实验结果表明:采用该模型在场景的地体幅度比小于-10dB的情况下,植被反演高度与实际高度的误差仅为0.03m,说明了模型的有效性。     

Initialization of Markov random field clustering of large remote sensing images

Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditions such as the choice of the number of clusters and their parameters. In this paper, an initialization scheme for MRF clustering approaches is suggested for remote sensing images. The proposed method derives suitable initial cluster parameters from a set of homogeneous regions, and estimates the number of clusters using the pseudolikelihood information criterion (PLIC). The method works best for an image consisting of many large homogeneous regions, such as agricultural crops areas. It is illustrated using a well-known polarimetric SAR image of Flevoland in the Netherlands. The experiment shows a superior performance compared to several other methods, such as fuzzy C-means and iterated conditional modes (ICM) clustering.     

合成孔径雷达简缩极化干涉数据的植被高度反演技术研究

该文提出了一种利用合成孔径雷达简缩极化干涉数据进行植被高度反演的三阶段方法。将全极化干涉数据处理中的相干最优方法引入简缩极化干涉数据处理,由相位最优相干系数对应的最优相位获得体去相干系数和地表相位的初始估计,利用相干区域边界提取方法更新体去相干系数,得到精确的植被高度估计值。采用欧空局提供的L波段的模拟全极化干涉数据构造简缩极化数据集,将该数据用于植被反演,反演结果验证了提出方法的有效性。     

基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究。首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV, VH, VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力。     

利用图谱特征解析和反演作物叶绿素密度

地面成像光谱仪可对作物个体及群体信息进行图谱同步解析,因此在农业定量化研究中具有巨大的应用潜力。利用可见-近红外成像光谱仪采集不同生育期玉米和大豆的冠层图谱数据,在逐步提取影像中光照土壤、阴影土壤、光照植被、阴影植被四种组分光谱的基础上,通过选取的敏感波段构建光谱植被指数和叶绿素密度进行波段自相关分析,探讨各个分量对作物叶绿素密度反演的影响。研究发现:当植被与土壤混合存在时,对叶绿素密度敏感的波段基本在红光与近红外波段;当植被光谱提纯后（剔除土壤光谱）,对叶绿素密度敏感的波段范围增大,表现在蓝、绿波段;当阴影叶片光谱剔除后,对叶绿素密度敏感的波段表现为可见光波段增加,近红外波段减少,红边波段决定系数最高。上述变化特征在不同作物中有相同的趋势,为探索地面成像光谱仪图谱协同反演作物生化参数进行了有意义的探索。     

Indoor wide-band polarimetric measurements on maize plants: a study of the differential extinction coefficient

A study of the wide-band polarimetric backscatter of maize plants, measured in laboratory conditions, is presented. The backscatter slant-range profiles in both linear (H-V) and Pauli basis manifest a higher extinction coefficient in the vertical channel due to the dominant vertical orientation of the structure of corn plants. The difference between the horizontal and vertical range profiles as the wave penetrates into the volume is employed to retrieve the differential extinction coefficient. In addition, the polarimetric target decomposition, as proposed by Cloude and Pottier, is used to obtain range profiles of alpha, entropy, and anisotropy. All these results reveal important features to be accounted for in the retrieval of the biophysical parameters of agricultural crops by means of polarimetric synthetic aperture radar interferometry.     

POLSAR Image Analysis of Wetlands Using a Modified Four-Component Scattering Power Decomposition

It is important to monitor environmental changes of the Earth's cover by remotely sensed data. This paper analyzes seasonal changes of a wetland by a modified polarimetric four-component scattering power decomposition method. The data sets analyzed here are L- and X-band fully polarimetric synthetic aperture radar (POLSAR) data, which have been acquired by the NICT/JAXA airborne polarimetric and interferometric synthetic aperture radar system in 2004. Since there existed a deficiency in the currently adopted decomposition schemes in that negative powers appear in a few pixels in the image analysis, we modified the approach taking into account physical conditions. It is shown by the modified scheme that the seasonal changes and features of the vegetation near Sakata Lagoon in Niigata, Japan, are observed clearly, demonstrating the utility of POLSAR image analysis for wetland assessments in general.     

基于Freeman分解的植被参数反演新方法

基于极化Freeman分解的极化协方差矩阵参数,极化干涉SAR互协方差矩阵可简化建模为植被高度、消光系数和地面干涉相位的函数。基于此,该文建立了以极化干涉SAR互协方差矩阵估算值与互协方差矩阵观测值之差为目标函数、以3个植被参数为未知量的优化模型,提出了基于Freeman分解的植被参数反演新方法。该方法避免了三阶段植被参数估计方法所面临的体相关系数确定问题,提供了一种独立于三阶段植被参数估计的新思路。仿真结果验证了新方法的有效性。     

Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR

In this paper, the potential of using polarimetric SAR (PolSAR) acquisitions for the estimation of volumetric soil moisture under agricultural vegetation is investigated. Soil-moisture estimation by means of SAR is a topic that is intensively investigated but yet not solved satisfactorily. The key problem is the presence of vegetation cover which biases soil-moisture estimates. In this paper, we discuss the problem of soil-moisture estimation in the presence of agricultural vegetation by means of L-band PolSAR images. SAR polarimetry allows the decomposition of the scattering signature into canonical scattering components and their quantification. We discuss simple canonical models for surface, dihedral, and vegetation scattering and use them to model and interpret scattering processes. The performance and modifications of the individual scattering components are discussed. The obtained surface and dihedral components are then used to retrieve surface soil moisture. The investigations cover, for the first time, the whole vegetation-growing period for three crop types using SAR data and ground measurements acquired in the frame of the AgriSAR campaign.      

数字全极化微波辐射计系统设计与定标分析

海洋风场是海洋与大气作用的重要参数之一。全极化微波辐射计是一种新型的微波遥感器。数字全极化微波辐射计采用多路数字相关技术,对水平和垂直极化信号进行相关处理,产生反演海面风场模型所需海面亮温Stokes矢量。详细介绍了数字全极化微波辐射计的系统设计方法,包括射频前端、中频段和数字相关器的设计。同时给出了系统内定标以及外定标方法。对数字全极化微波辐射计做了细致的理论分析和硬件实现设计。     

多角度极化SAR图像散射特征建模及其应用

本文将基于条带观测模式的极化SAR散射模型拓展至方位向多角度观测模式,基于典型极化散射类型组合提出一种非各向同性散射特征模型。该模型参数纬度多且随方位向观测变化,需要替代性方法提取多角度极化散射特征。首先,采用基于Wishart分布的统计量对非各向同性散射中心进行检测,并逐像素生成基于散射特征差异的新序列图像。其次,以新序列图像作为处理对象,提取极化似然比序列、子孔径角度序列、极化熵—似然比序列、极化散射角—似然比序列、极化各向异性度—似然比序列。最后,集成特征序列编码及支持向量基（SVM）方法进行分类。通过机载P波段极化SAR开展360°观测试验,验证了方法的有效性并揭示出在地物分类方面的应用潜力。