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投稿时间：2007-06-06 修订日期：2007-08-05
投稿时间：2007-06-06 修订日期：2007-08-05
中文摘要: 提出了一种基于支持向量机的卫星遥感数据大雾判识方法：首先通过对风云
1D卫星大雾区域的各通道辐射值出现频次进行概率统计，利用其阈值来粗判识大雾；然后在
粗判识的基础上通过支持向量机的方法进行大雾细判识；最后利用腐蚀和膨胀的图像处理技
术对判识后的图像进行优化处理。在对我国2006年9－12月的65条监测到大雾的风云1D轨道
的探测数据进行分析之后，发现大雾判识结果与专家标记吻合。检验结果表明，利用1、2、
4、6、7、10通道组合进行粗判识的结果最好，5交叉正确率为89.9849%，TS评分为74.04%。
利用上述方法对个例的分析检验表明，基于支持向量机的遥感大雾判识方法是切实可行的。
Abstract:A method is put forward to recognize the fog based on the support vect
or machine, according to the satellite remote sensing data. Firstly, the probabi
lity statistics method is used to roughly judge the fog, according to the freque
ncy of the fog areas appearing at different channels of FY 1D satellite; second
ly, based on the former judgment, the support vector machine is applied to judge
the fog carefully; lastly, erosion and dilation techniques are used to optimize
the result of the second procedure. From September to December in 2006, 65 over
passes of FY 1D satellite data including fog areas are analyzed, and the judged
fog areas are found to correspond well to the experts' experience. And the resu
lt shows that the combination of 1, 2, 4, 6, 7 and 10 channels is the best of ju
dgment. The 5 fold cross validation is 89.9849% and the TS score is74.04%. This method is also used to recognize the fog during other time, and fou
nd that this method is excellent.
keywords: fog judgment by remote sensing data probability statistics support vector mach
ine erosion and dilation
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引用文本：
刘年庆,蒋建莹,吴晓京,2007.基于支持向量机的遥感大雾判识[J].气象,33(10):73-79.
Liu Nianqing,Jiang Jianying,Wu Xiaojing,2007.Fog Judgment Based on the Support Vector Machine by Remote Sensing Data[J].Meteor Mon,33(10):73-79.
刘年庆,蒋建莹,吴晓京,2007.基于支持向量机的遥感大雾判识[J].气象,33(10):73-79.
Liu Nianqing,Jiang Jianying,Wu Xiaojing,2007.Fog Judgment Based on the Support Vector Machine by Remote Sensing Data[J].Meteor Mon,33(10):73-79.