基于径向基函数网络的云自动分类研究
投稿时间:2001-12-20  修订日期:2002-05-15  点此下载全文
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作者单位
蒋德明 南京气象学院, 电子工程系, 江苏, 南京, 210044 
陈渭民 南京气象学院, 电子工程系, 江苏, 南京, 210044 
傅炳珊 南京气象学院, 电子工程系, 江苏, 南京, 210044 
王建凯 南京气象学院, 电子工程系, 江苏, 南京, 210044 
基金项目:国防预研基金项目
中文摘要:采用GMS-5红外(10.5-12.5μm)和可见光(0.55-0.9μm)两通道资料,采集了1999年7-10月中国东南沿海57区、58区和59区包括晴空在内的12类云目标样本2912个,采样窗尺寸为8×8像素,随机生成训练和测试两个样本子集。对径向基函数网络(radial base function neural network,RBF)在云分类问题研究中的应用价值进行了全面的测试与分析,得到了肯定的结论,提出了优化设计的方法。对6类云型分类试验,平均正确率为86%;对11类云型分类试验,平均正确率为67%。采用自组织竞争神经网络实现寻找RBF神经网络的隐层神经元中心。在特征空间生成过程中,采用小波包分解算法实现模式特征抽出。结果表明,小波包分解特征能很好地描述不同云型的差异。
中文关键词:云分类  神经网络  卫星图像
 
A Neural Network Approach to the Automated Cloud Classification of GMS Imagery over South-East China Maritime Regions
Abstract:This paper presents an automated and efficient cloud classification scheme based on radial base function neural network (RBF),which has an average classification accuracy of more than 86% for less than 5 cloud patterns and 67% for more than 10 cloud patterns.An additional self-organized competitive neural network is also suggested to find out the center of the hidden layer neurons of RBF network,which greatly ameliorates the efficiency of the RBF classifier.Features abstracted by using the two-dimensional wavelet packet 3-level decomposition provide essentials for the description of cloud patterns,thus improving the capability of pattern recognition of neural network classifier.The input dataset employed in the scheme are defined by 2912 samples of 8×8 pixels size taken in July-October 1999 over SE China maritime regions from the visible(0.55-0.9μm) and infrared(10.5-12.5μm) channels of Geostationary Meteorological Satellite 5(GMS-5).These images in Lambert Conformal Conic Projection and in a coarsened resolution of 13.03km×13.22km for both channels are downloaded from the meteorological operational networks.Each of the samples in the dataset is classified into one of the eleven predefined classes in accord with the SYNOP codes used in weather reports.
keywords:cloud classification  neural network  satellite imagery
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