云分类

利用FY-2C资料对西北太平洋海域云分类的研究

A Study of Cloud Classification in the Northwestern Pacific by Using FY-2C Data

双光谱云图的云分类探讨

Research on the cloud classification for the bi-spectrum cloud picture

GMS双光谱云图云分类微机处理系统

A microcomputer processing system for Cloud Classification Using GMS bispectral satellite images

双光谱卫星云图的模糊推理云分类

Cloud Fuzzy Inference and Classification based on Double-Spectrum Satellite Images

多光谱云分类技术在锋面云系中的应用

Application of multi-spectral cloud classification technique in frontal cloud system

云分类中逐个修改聚类和模糊聚类分类性能的对比研究

A Comparative Study on Stepwise Cluster and Fuzzy Cluster in Cloud Classification Techniques

基于模糊纹理光谱的全天空红外图像云分类航空红外天文望远镜

Cloud Classification of the Whole Sky Infrared Image Based on the Fuzzy Uncertainty Texture Spectrum

基于云分类和降水先验知识的卫星云图降水估计

A Rainfall Estimation Method of Satellite images Based on The Cloud Classification And The Precipitation Experiential Knowledge

使用云分类和降水率估计的方法，用静止气象卫星数字云图进行面雨量估算。

He method of cloud classification and rainfall rate calculation are used in areal rainfall estimation of GMS imagery data .

该软件可以实现点云分类显示、不规则三角网构建、点云分类分割等功能。

It can display with classified point cloud , build triangulated irregular network and classify point cloud and other functions .

第二，每个数据的类别标签参考国家卫星气象中心的云分类产品结果，将实验数据划分为地表、混合像元、高层云或雨层云、卷层云、密卷云、积雨云以及层积云或高积云7类。

Secondly , the research data , which use the cloud classification productions of National Satellite Meteorological Center as types ' labels , are divided into Land , Mixed cloud , Altostratus or Nimbostratus , Cirrostratus , Cirrus Spissatus , Cumulonimbus and Stratocumulus or Altocumulus .

进行云分类和云团分析预测的技术与方法研究，客观、准确、及时地进行云图识别和云区域估计等研究，是气象学应用中主要的研究方向。

The study on the technology and methods for clouds classification and clouds analysis prediction , and the research on how to identify nephogram and estimate clouds regional objectively , precisely and timely , which are main research directions in the application technology on the meteorology .

基于数学形态学原理的Lidar点云数据分类在植被信息分离中的应用

Application of Lidar Point Data-Set Classification in Vegetation Information Remove Based on Principle of Mathematical Morphology

用多谱阈值法进行GMS-5卫星云图云型分类的研究

Cloud Classification of GMS-5 Satellite Imagery by the use of Multispectral Threshold Technique

用神经网络方法对NOAA-AVHRR资料进行云客观分类

Cloud classification for NOAA-AVHRR data by using a neural network

利用GMS-5红外资料进行云的分类识别

Classification of Cloud Using GMS-5 Infrared Data

本文紧紧围绕机载LiDAR点云数据分类这一主题，重点研究了机载LiDAR数据滤波、非地面点云分类和地面点云分类等方面的内容，在理论和算法上取得了一定进展。

This dissertation focuses on the theme of airborne LiDAR data classification , and puts keystone on the study of airborne LiDAR data filtering , non-ground point cloud classification and ground point cloud classification , which gets along in theory and arithmetic to some extent .

云变换分类也取得了和云模型分类一样高的分类精度，但是云变换分类计算复杂、耗时。

Cloud transformation classification made high accuracy as the cloud model classification . However , this method is computational complexity and time-consuming .

为提高雷达定量测量降水的精度，利用武汉CINRAD/SA雷达反射率数据，研究提出了对流性降水和层状云降水自动分类算法（ACSS）。

To increase the accuracy of radar rainfall estimation , an automatic convective / stratiform precipitation classification algorithm based on radar reflectivity data at the Wuhan radar station is proposed .

用雷达反射率作对流性降水和层状云降水自动分类

Automatic Convective / Stratiform Cloud Precipitation Classification Based on Radar Reflectivity

在云实科的分类系统中是有选择的名称。

Alternative name in some classification systems for the family caesalpiniaceae .

培育先锋后备森林资源是森林可持续发展的基础&谈云冷杉林的分类经营

Discuss on the base of forest sustainable development

以本论文中提出的激光雷达点云数据过滤和分类方法为基础，提出了建筑物点云提取的方法。

From the above proposed filtering and classification approaches of LiDAR data in this thesis , the process of building extraction is summarized .

利用探空和雷达回波资料对层状云降雨进行了分类。

Based on the data of sounding and 711 radar echo , the precipitable stratiform clouds in the middle of Shaanxi Province are analyzed , and classified .

本文主要研究了基于变分方法的云的判别和云分类的方法。

In this thesis , we study methods of cloud differentiation and cloud classification based on variational PDEs .

首先通过云变换产生各类地物的云模型然后根据与云模型的相同分类步骤进行分类，最后对云分类结果进行了分析。

Firstly , the cloud models of each cluster were produced by cloud transformation in this method . Secondly , the rest of steps are same to the cloud model classification .

将4种基本云类（卷云、积雨云、积云和层云）的分数维和灰度梯度共生矩阵（GGCM）的二次统计特征结合起来，对云类进行分类与识别。

Combined the fractal dimension with the 2 order statistical features of Gray Gradient Co occurence Matrix ( GGCM ) of 4 kinds of basic clouds pictures ( Cirrus , Cumulus , Cumulonimbus and Layer Cloud ), the clouds are classified and recognized .