| 用神经网络方法对NOAA-AVHRR资料进行云客观分类 |
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| 引用本文： | 师春香,瞿建华.用神经网络方法对NOAA-AVHRR资料进行云客观分类[J].气象学报,2002,60(2):250-255. |
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| 作者姓名： | 师春香 瞿建华 |
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| 作者单位： | 中国气象局国家卫星气象中心,北京,100081 |
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| 基金项目： | 国家重点基础研究项目《我国重大天气灾害形成机理和预测理论研究》(0 5 0 1 0 6) |
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| 摘 要： | 利用NOAA AVHRR 5个通道资料建立了 6种云类以及陆地和水体的样本数据库 ,其中包括 8× 8象素样本和单象素样本。AVHRR的 5个探测通道都位于大气窗区 ,吸收物质少 ,比较透明 ,可以比较准确地反映探测表面的性质。理论分析和试验结果表明 :除了不同性质的云在 5个通道中有不同的表现外 ,通道之间的差别也可用于云分类。在理论分析和试验的基础上 ,对 8× 8象素样本库提取了包括光谱特征、灰度特征、通道差特征、灰度统计量和灰度直方图统计量特征在内的 80个特征 ,并利用逐步判别分析方法进行特征筛选 ,共选出 2 0个特征 ,用神经网络方法对 8种类型云和地表样本数据库分类 ,选择网络结构为 2 0 - 4 0 - 15 - 4的B P网络 ,利用 30 0 0多个样本进行神经网络训练 ,并用其余的 3万多个独立样本数据进行检验 ,测试正确率达 79%。类似地 ,对单象素样本数据 ,提取了包括光谱特征、灰度特征、通道差特征在内的 2 0个特征 ,用神经网络方法对 8种类型云和地表分类 ,选择网络结构为 2 0 - 4 0 - 15 - 4的 4层B P网络 ,利用 2 0 0 0多个样本进行神经网络训练 ,并用其余的 2万多个独立样本数据进行检验 ,测试正确率达 78%。设计并编写了实际云图客观云分类系统和软件 ,该系统输入为 5个通道的AVHRR数据 ,可自动获取已
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| 关 键 词： | AVHRR资料 云和地表分类 神经网络 |
| 收稿时间： | 2000/5/30 0:00:00 |
| 修稿时间： | 2000年5月30日 |
CLOUD CLASSIFICATION FOR NOAA-AVHRR DATA BY USING A NEURAL NETWORK |
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| Shi Chunxiang and Qu Jianhua.CLOUD CLASSIFICATION FOR NOAA-AVHRR DATA BY USING A NEURAL NETWORK[J].Acta Meteorologica Sinica,2002,60(2):250-255. |
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| Authors: | Shi Chunxiang and Qu Jianhua |
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| Institution: | National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081;National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081 |
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| Abstract: | Sample database of clouds, land and water was built based on NOAA-AVH RR 5-channel data which in-clude more than thirty thousand 8×8 pixel samples and more than twenty thousand single pixel samples. The five AVHRR channels are in the at mospheric windows which have a little absorptive mass and are rather trans-parent. They can show characters of detected surface well. Channel 1 (CH 1) is in the visible wave range, CH2 in the near infrared wave range, and CH3, CH4, CH5 are in the inf rared wave range. Theoretical analyses and experiments show t hat not only 5-channel dat a can be used to distinguish clouds, land and water, but also the difference between channels can do so. For example, CH4-CH5 can be used to distinguish water particle cloud and ice particle cloud because the big gest absorption difference between water particle and ice particle is near 12 L m. On the basis of theoretical analyses and experiments, 80 features were extracted from 5-channel AVHRR data for 8×8 pixel samples, which involve spectrum feature, gray feature, channel difference feature, the gray statistical feature and the gray histogram statistical feature. 20 features were selected by using the distinguishing analysis step-by-step method. Classification experiment of sample database was done by using neural net work method. We designed a neural network model with 20 inputs, 2 hidden layers and 4 out puts(20-40-15-4). More then three thousand samples selected randomly were used to train the neural net work model. The other independent samples were used to test. And we got at esting accuracy of 79%. Similarly, for single pixel samples, 20 features including spectrum feature, gray feature and channel difference feature were extracted from 5-channel AVHRR data. Classification ex periment of sample database was made by using neural net work method. We desig ned a neural net work model with 20 inputs, 2 hidden layers and 4 out puts(20-40-15-4). More than two thousand samples selected randomly were used to train the neural network model. The ot her independent samples were used to test. And we gota testing accuracy of 78%. Some different disposals were used to 8×8 pixel samples database and single pixel sample database. |
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| Keywords: | AVHRR data Classification of cloud and surface Neural network |
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