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。     

基于GA-WNN的极化SAR海洋溢油检测方法研究

海洋溢油对海洋生态和人类生活带来严重的影响。由于合成孔径雷达(Synthetic Aperture Radar,SAR)具有全天时全天候的工作能力,在海洋溢油检测中发挥重要作用。目前,极化SAR是SAR探测技术的先进手段。本文利用6个极化特征进行溢油检测,通过对比分析这些特征对不同溢油的检测能力,得出单一极化特征在溢油检测中存在不足。通过J-M特征优选方法,提取出溢油检测识别度较高的特征影像,并利用遗传算法优化的小波神经网络(Genetic Algorithm-Wavelet Neural Network,GA-WNN)进行溢油检测。利用2套Radarsat-2全极化数据进行了方法验证,结果表明,该方法优于其他检测方法,溢油检测精度分别达到90.31%和95.42%。     

基于偏振雷达的积层混合云降水增雨潜力识别方法研究

本文统计分析了北京地区近三年的有效降水,重点研究了积层混合云降水特点并对其分类,发现积层混合云降水出现频次约占总降水次数的61%,其中积层混合云降水以积层连结型和水平混合型为主,二者之和占近80%。重点分析了积层混合云中对流和层云两种不同特点降水类型的宏微观结构,确立了反射率因子Z、温度T、粒子含水量M、催化剂AgⅠ（碘化银）活化率NE和粒子相态HTC（hydrometeor type classification）为人工增雨潜力识别指标及这些识别指标的取值范围,同时也根据研究现状和人工影响天气需求总结制定出人工增雨潜力等级。利用偏振雷达构建模糊逻辑识别算法对积层混合云三种降水类型进行增雨潜力区域识别研究,结果表明:（1）对于播撒碘化银增雨来说,积层混合云的增雨潜力区在垂直方向上可分为上、中、下三层,上层（增雨等级为“不适合”）和下层（零度层及以下）分别受含水量和温度等影响不适合增雨,中间层（增雨等级大于等于“等级一”）是可增雨区域;（2）积层混合云中层云区增雨潜力较小,对流云区可增雨潜力要远大于层云区,开式流场型与积层连结型可增雨潜力要大于水平混合型;（3）当降水云中识别出霰粒子时,其附近的大部分区域会有较好的增雨潜力。通过偏振雷达实例检验和数值模式模拟在积层混合云不同部位播撒碘化银催化试验发现,在增雨潜力较好的区域催化有很明显增雨效果,模拟试验结论与偏振雷达识别增雨潜力区结果也基本一致,说明基于偏振雷达的增雨潜力区识别方法和结果是具有参考意义的。     

Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment

This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.     

2019年8月16日诸城超级单体风暴双偏振参量结构特征分析

利用青岛S波段双偏振多普勒天气雷达探测资料和常规气象观测资料以及区域自动气象站观测资料,对2019年8月16日发生在山东省诸城市的一次长寿命超级单体风暴双偏振结构特征进行了分析。结果表明:超级单体风暴发生在东北冷涡和地面中尺度辐合线共同作用背景之下,对流有效位能偏低,但风垂直切变非常强,这种配置有利于超级单体风暴的形成与维持。诸城超级单体风暴持续3 h左右并伴有深厚持久的中气旋,旺盛阶段最强反射率因子、基于单体的垂直累积液态含水量、强中心高度和单体顶部高度平均分别为74.1 dBz、67.9 kg/m2、6.3 km和11.3 km。对偏振特征分析表明,风暴低层60 dBz以上回波区对应偏小的差分反射率（Z_dr）、小的相关系数（CC）和大的差分相移率（K_dp）,湿（或干）冰雹和液态雨滴共存。此外,低层入流缺口附近有明显Z_dr弧存在。风暴中层强上升气流区内有明显的有界弱回波区,其顶部达到7 km左右。有界弱回波区内相关系数较小,其周围有明显的Z_dr环和CC环,Z_dr环顶部达到?10℃层高度。0℃层高度之上存在深厚的Z_dr柱和K_dp柱,顶部都达到?20℃层高度,Z_dr柱位于有界弱回波区东侧,K_dp柱位于西侧。?10—?20℃层,Z_dr柱对应强的水平极化反射率因子（35—60 dBz）和小的差分相移率,表明含有少数偏大的液态或湿冰粒子,而K_dp柱对应更强的水平极化反射率因子（55—72 dBz）和小的差分反射率,表明含有一定数量的小的液态或（和）湿冰粒子及大的冰雹粒子。风暴低层强反射率核后侧径向上如果出现显著差分反射率负值区,可作为特大冰雹（直径≥50 mm）的识别依据;如果对应异常大的差分相移率,表明含有浓度较高的雨滴和包有水膜的冰雹粒子。      

Co-polarization channel imbalance determination by the use of bare soil

This paper describes a novel technique which determines the co-polarization channel imbalance by the use of natural bare soil, instead of a trihedral corner reflector (CR). In polarimetric synthetic aperture radar (PolSAR) remote sensing, the polarimetric calibration (PolCAL) is the key technique in quantitative earth parameter measurement. In general, the current PolCAL process can be separated into two parts. The first part tries to estimate the crosstalk and the cross-polarization (x-pol) channel imbalance components by the reflection symmetry and the reciprocity properties, without a CR. Then, at least one trihedral CR is required to determine the co-polarization (co-pol) channel imbalance; however, it is not always possible to deploy a CR in difficult terrain such as desert. In this paper, we utilize bare soil as a stable reference target, and four common natural constraints of bare soil are evaluated to determine the co-pol channel imbalance, without the use of a CR. It should be mentioned that we do not propose to replace the CR by a natural target, but we utilize the natural target to enhance the PolCAL accuracy when a CR is missing. The four constraints are: (1) the consistency of the polarimetric orientation angle (CPOA) between the PolSAR POA and the digital elevation model (DEM) derived POA; (2) the unitary zero POA (UZPOA) of a flat ground surface; (3) the zero helix (ZHEX) component of the ground surface; and (4) the unitary version of the previous zero helix (UZHEX). In the theoretical part of this paper, we demonstrate that the forth constraint is the most suitable in different scenes. We then propose a multi-scale algorithm to further improve the robustness of the co-pol channel imbalance determination. In the experimental part, we apply our new methods to simulated airborne SAR (AIRSAR) and real uninhabited aerial vehicle SAR (UAVSAR) data. Without the use of any CR, the recovered results show that the estimated amplitude and phase error of the co-pol channel imbalance are less than 0.5 dB and 5°, respectively.     

极化雷达目标信息分解技术及其在古湖岸线探测中的应用

目标信息分解就是将地物雷达电磁波回波的复杂散射过程分解为几种单一的散射过程,每种散射过程都有一个对应的散射矩阵,利用基于地物相关矩阵特征向量分解的目标分解方法,可以将目标相关矩阵分解为三种单散射体相关矩阵的加权和:单向散射,双向散射,交叉散射。将目标分解理论应用于极化成像雷达数据,可以分别得到对应于地物单向散射(奇次散射)、双向散射(偶次散射)及多向散射机制的雷达后向散射系数。本研究应用目标矩阵分解技术对青藏高原西昆仑阿克赛钦湖地区SIR-CL波段和C波段全极化多视复矩阵(MLC)数据进行处理,得到研究区地表单向散射、双向散射及多向散射后向散射系数图像。研究发现,不同性质地表具有不同电磁波散射机制,散射分解后的图像能有效去除噪声,而且能够区分具有不同散射机制的地表,对研究区多期次古湖岸线的识别有重要意义。     

偏振微波雷达探测大气研究进展及几个问题的考虑

近年国际上偏振微波雷达探测大气研究有了重要进展,成为雷达气象学研究的新热点,并可能成为天气雷达发展的重要方向。介绍了微波圆偏振技术现状和双线偏振测量技术体制的重要进展,以及近年在气象应用中所获得的最新研究结果,展望了偏振探测技术的应用前景。进一步讨论了开展偏振探测技术研究所应考虑的有关偏振类型、实现体制、方案设计、产品算法和波段选择中的问题,对偏振探测技术研究具有参考价值。     

The potential of linear discriminative Laplacian eigenmaps dimensionality reduction in polarimetric SAR classification for agricultural areas

In this paper, the linear discriminative Laplacian eigenmaps (LDLE) dimensionality reduction (DR) algorithm is introduced to C-band polarimetric synthetic aperture radar (PolSAR) agricultural classification. A collection of homogenous areas of the same crop class usually presents physical parameter variation, such as the biomass and soil moisture. Furthermore, the local incidence angle also impacts a lot on the same crop category when the vegetation layer is penetrable with C-band radar. We name this phenomenon as the “observed variation of the same category” (OVSC). The most common PolSAR features, e.g., the Freeman–Durden and Cloude–Pottier decompositions, show an inadequate performance with OVSC. In our research, more than 40 coherent and incoherent PolSAR decomposition models are stacked into the high-dimensionality feature cube to describe the various physical parameters. The LDLE algorithm is then performed on the observed feature cube, with the aim of simultaneously pushing the local samples of the same category closer to each other, as well as maximizing the distance between local samples of different categories in the learnt subspace. Finally, the classification result is obtained by nearest neighbor (NN) or Wishart classification in the reduced feature space. In the simulation experiment, eight crop blocks are picked to generate a test patch from the 1991 Airborne Synthetic Aperture Radar (AIRSAR) C-band fully polarimetric data from of Flevoland test site. Locality preserving projections (LPP) and principal component analysis (PCA) are then utilized to evaluate the DR results of the proposed method. The classification results show that LDLE can distinguish the influence of the physical parameters and achieve a 99% overall accuracy, which is better than LPP (97%), PCA (88%), NN (89%), and Wishart (88%). In the real data experiment, the Chinese Hailaer nationalized farm RadarSat2 PolSAR test set is used, and the classification accuracy is around 94%, which is again better than LPP (90%), PCA (88%), NN (89%), and Wishart (85%). Both experiments suggest that the LDLE algorithm is an effective way of relieving the OVSC phenomenon.