| 基于支持向量机的CloudSat卫星云分类算法 |
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| 引用本文： | 韩丁,严卫,任建奇,赵现斌.基于支持向量机的CloudSat卫星云分类算法[J].南京气象学院学报,2011,34(5):583-591. |
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| 作者姓名： | 韩丁 严卫 任建奇 赵现斌 |
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| 作者单位： | 解放军理工大学气象学院,江苏南京,211101 |
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| 基金项目： | 国家自然科学基金资助项目(41076118) |
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| 摘 要： | 从基于云角色的分类思想出发,利用星载毫米波雷达探测资料提取云的特征参数,建立支持向量机（support vector machine,SVM）模型实现云的分类。通过与BP（back propagation）网络模型的分类结果进行对比,发现两种模型都具有较好的分类能力,但SVM模型的识别准确率更高,计算速度更快。基于CloudSat资料的云分类实例表明,SVM模型的分类结果与CloudSat数据处理中心（Data Processing Center,DPC）发布产品具有很好的一致性。
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| 关 键 词： | 支持向量机 BP网络 云分类 毫米波 CloudSat |
Cloud type classification algorithm for CloudSat satellite based on Support Vector Machine |
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| HAN Ding,YAN Wei,REN Jian-qi,ZHAO Xian-bin.Cloud type classification algorithm for CloudSat satellite based on Support Vector Machine[J].Journal of Nanjing Institute of Meteorology,2011,34(5):583-591. |
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| Authors: | HAN Ding YAN Wei REN Jian-qi ZHAO Xian-bin |
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| Institution: | HAN Ding,YAN Wei,REN Jian-qi,ZHAO Xian-bin(Institute of Meteorology,PLA University of Science and Technology,Nanjing 211101,China) |
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| Abstract: | According to the role-based cloud classification method,a SVM(Support Vector Machine) model is established to achieve cloud type classification by extracting characteristic parameters of spaceborne millimeter-wave radar sounding data.By comparing with the classification results using BP(back propagation) network model,it is found that both two models have good classification capability,but the SVM model has better identification accuracy and faster calculation speed.An example of cloud type classification b... |
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| Keywords: | Support Vector Machine BP network cloud classification millimeter wave CloudSat |
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