基于纹理特征的地基云分类识别研究  

Ground-based cloud classification and recognition based on textural features

作　　者：李含光[1] 王琦[1] 

机构地区：[1]南京信息工程大学计算机与软件学院,江苏南京210044

出　　处：《重庆邮电大学学报（自然科学版）》2014年第4期541-545,共5页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)

基　　金：国家自然科学基金青年科学基金(61202137)~~

摘　　要：云是一种重要的天气现象,云观测对当时天气系统的观测具有很强的指导意义,同时云的变化趋势也是预测未来天气的一项重要指标。目前对遥感卫星云图的研究取得了较丰富的成果,但地基云图分类识别研究取得的成果则比较有限,地基云图的研究一直是模式识别中的难点。采用标准气象站地基云图数据,选取了浓积云、积雨云、雨层云等5类常见的云类,并将Gabor小波变换应用于地基云图的纹理特征数据提取,运用实验法选取了Gabor小波变换的最优参数。将地基云图纹理特征数据运用到最短距离和包含BP神经网络的陀螺分类器构造成的地基云图分类系统,从而实现了对地基云图的分类。实验结果表明,该方案不仅能够有效地运用于地基云图分类,而且分类效果也是比较理想的。Cloud is an important weather phenomenon. Cloud observations have great directive significance on the observa- tion of weather system. The variation trend of clouds is also an important indicator to predict weather in the future. At pres- ent the study of satellite cloud imagery has achieved great success. But the research of ground-based cloud is not perfect; it is difficult work in pattern recognition. In this paper, the ground-based cloud data is from a standard meteorological station. We select five categories of clouds. Gabor wavelet transform is used in texture features extraction of ground-based nephogram. Experimental method is used for selecting the optimal parameters of Gabor wavelet transform. The ground-based nephogram classifier is constructed by neural network which is through the shortest distance and gyro classifier. Experimental results show that this scheme achieves good results in ground-based nephogram classification.

关 键 词：地基云 纹理特征 小波变换 陀螺分类 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]