吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于卫星云图的DBSCAN聚类云团分类方法

王猛1,2, 何丽莉1,2, 白洪涛2,3, 欧阳丹彤1,2   

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012;2. 吉林大学 符号计算与知识工程教育部重点实验室, 长春 130012;3. 吉林大学 公共计算机教学与研究中心, 长春 130012
  • 收稿日期:2015-01-05 出版日期:2016-01-26 发布日期:2016-01-19
  • 通讯作者: 何丽莉 E-mail:helili@jlu.edu.cn

DBSCAN Clustering Cloud Classification MethodBased on Satellite Images

WANG Meng1,2, HE Lili1,2, BAI Hongtao2,3, OUYANG Dantong1,2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China; 3. Center for Computer Fundamental Education, Jilin University, Changchun 130012, China
  • Received:2015-01-05 Online:2016-01-26 Published:2016-01-19
  • Contact: HE Lili E-mail:helili@jlu.edu.cn

摘要:

针对云分类问题提出一种新的云团分类方法. 该方法先利用风云二号静止气象卫星实时云图图像资料建立多种云和地表类型的样本库, 提取分析已知样本的光谱特征和纹理特征; 再使用中值滤波器对云图进行预处理, 并采用具有噪声的基于密度的聚类算法对云区聚类; 最后对聚类得到的云团光谱特征和纹理特征进行匹配, 确定云团所属的云类别. 实验结果表明, 该方法以云团为单位进行划分, 易实现云团分类自动化.

关键词: 云团分类, 光谱特征, 纹理特征, 卫星云图

Abstract:

According to the cloud classfication problem, we put forward a new cloud classification method. Firstly, we established a sample database of multiple clouds and surface types by using realtime cloud image data of FY2 geostationary meteorological satellite, and extracted the spectral features and texture features of known samples. After pretreating the cloud image by median filter, we clustered on the cloud area by using an algorithm based on density clustering algorithm with noise. Finally, we matched spectral features and texture features of the cloud, and determined the type of cloud. The experiment
shows that the method, with clouds as the unit, is easy to realize automation of cloud classification.

Key words: cloud classification, spectral feature, texture feature, satellite cloud image

中图分类号: 

  • TP311