Using S-band dual polarized radar for convective/stratiform rain indexing and the correspondence with AMSR-E GSFC profiling algorithm

The separation of rain types in convective and stratiform regimes has long been a goal in microwave remote sensing of precipitation research. In this essence, a dual polarized radar based indexing scheme that provides information on convective and stratiform (C/S) rain regimes has been presented in correspondence with advanced microwave scanning radiometer – earth observing system (AMSR-E) GSFC profiling algorithm estimate of convective rain percentage. The dual polarized radar based C/S indexing scheme first retrieves the normalized gamma drop size distribution parameters, median volume drop diameter (D₀) and concentration parameter (N_w), from dual polarized radar measurements Z_H and Z_DR, representing reflectivity and differential reflectivity respectively, by means of the genetic programming approach. Next, the C/S rain index is calculated based on the formulation of an empirical relation in N_w–D₀ domain. The scheme has been inspected and applied on measurements from the S-band Chilbolton dual polarized radar. A considerable number of “coincident” cases from the radar and the AMSR-E observations are investigated. It has been revealed that the dual polarized radar based C/S rain indexing is in a similar pattern with the AMSR-E GSFC profiling algorithm estimate of convective rain percentage. Generally, as C/S rain index value increases, which signifies a stratiform to convective trend, the AMSR-E convective rain percentage also increases.     

离心式分级机分级理论与分级效能研究

介绍了离心式分级机的工作原理，分析了离心式分级机的流场特性，重点研究了速度特性和压力特性的分布规律，对离心机的分级效能以及改进方案进行了探讨，论述了细粉出口附加二次引风以及二相流入口速度对提高分级效率的的影响。     

基于层次分析法的直扩信号安全性能评估模型

在综合直扩信号风险分析、截获方法的基础上，率先对直扩信号安全性能评估模型进行了研究，建立了基于AHP（AnalyticHierarchyProcess，层次分析法）的直扩信号安全性能评估模型，详细给出了直扩信号安全指数的计算过程，并对多种直扩信号的安全指数进行了仿真。仿真结果袁明：评估方法有效；提出的模型可以定量评价不同直扩信号的安全性能，为直扩信号的安全性设计提供定量的依据。此外，还提出了卫星测控直扩信号的安全等级分类原则和卫星安全等级要求。     

波音737系列飞机结构修理类型分类方法

根据FAA颁发的AASR的要求,2010年12月20日以后,波音737-系列飞机的维护计划中需包含损伤容限的基本检查要求。波音依据AASR的要求对需要进行损伤容限分析的结构修理的批准实施了新的方法。新方法的实施,导致波音对结构修理类型分类法方法和批准方式的改变。     

基于主分量分析的多维时间序列特征选择方法研究

研究了多维时间序列的特征选择问题。首先,讨论了主分量分析的原理;然后,将提出的斜率趋势算法和核函数分类算法应用于多维时间序列的特征选择过程中,并探讨了两类方法的优缺点;最后,采用两种算法对目标距离像多维时间序列进行了仿真实验。实验结果表明了所应用方法的可行性和有效性.     

基于图论分割的多光谱图像非监督分类方法

针对传统基于像素的多光谱遥感图像分类方法存在的"麻点"现象、采样成本高等问题,提出了一种基于图论分割的非监督分类方法,首先采用基于图论的分割算法,按局部邻近相似像素点分割成若干子区域,再以分割后子区域为基本单元,整体进行模糊 C均值聚类,最终实现对多光谱图像的非监督分类.实验证明,该方法结合了局部邻近像素点的相互关系以及相似区域的整体特征,有效解决了麻点问题,具有较高的分类精度和算法效率,降低了采样成本.     

基于支持向量机的无参考遥感图像质量评价方法

针对遥感图像主观评价方法的低效率以及常用客观评价方法无法充分考虑人眼对图像的感知特性的问题,文章提出了一种基于支持向量机的无参考遥感图像质量（quality）评价方法。首先建立遥感图像主观评价库,然后在不需要图像失真信息的基础上,利用支持向量机（SVM）将图像的失真类型分为三类,并对每类进行单项评价,再通过加权得到遥感图像的总评分,最后将本文方法、信噪比与信息熵的评价结果回归到主观评价空间并进行对比。实验证明,文章所提方法能客观地评价遥感图像的质量,且优于信噪比和信息熵两种质量评价方法,其结果与人眼视觉感受相符。     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.     

基于双重分类深度学习的低空目标自动检测方法

由于图像数量多,因此准确、高效的目标检测是提升靶场光测图像处理自动化程度的关键步骤.针对低空目标图像及目标类型多、目标特性变化等情况导致传统目标检测算法适应性差的问题,提出了一种基于双重分类深度学习的低空目标自动检测方法.该方法基于深度学习目标检测框架YOLO V3,根据低空目标的亮度和形状的双重属性特征,将网络输出层...     

基于多种空间信息的高光谱遥感图像分类方法

在高光谱遥感图像分类方法中,空间特征和光谱特征的融合可以有效地改善分类效果。针对单一空间特征的信息表达不充分问题,提出了一种联合多种空间特征的高光谱图像空谱分类方法。利用超像素信息对分类结果进行后处理去掉椒盐噪声,并创造性地将超像素信息应用于分类前处理,提出了一种利用超像素信息对像素点的特征向量进行线性加权融合的方法。试验结果表明,所提方法的性能优于目前的通常方法。