云图的自动分类是实现地基云自动化观测的技术保障.该文探讨了一种先将云图分为积状云、层状云和卷云3大类的分类方案,通过对3大云类和晴空这4种天空类型的纹理特征、颜色特征和形状特征进行分析,选取了21个特征参量,并采用K最近邻分类器(K-Nearest Neighbor,KNN),在不同的K取值情况下对这几类天空类型进行了分类识别.结果表明:新的分类方案是可行的,且当纹理特征、颜色特征和形状特征结合使用时获取了比单独利用纹理特征、颜色特征和形状特征以及它们两两组合时更好的识别效果.当K=7且使用21个特征参量时,KNN算法对积状云、层状云、卷云和晴空的识别最好,识别正确率分别为91.1％,74.4％,70.0％和100.0％,平均正确率为83.9％.%Cloud plays an important role in the meteorological research, and it is one of the most important factors of earth's energy balance and hydrological cycle. In order to actualize the automatic ground-based observation of clouds, automatic classification of cloud image is a difficult problem.rnA cloud classification scheme which classifies the cloud images into cumulus, stratus and cirrus is discussed. The clear sky is considered as a separate category in the scheme. Three kinds of image features, texture, color and shape are analyzed. The texture features describe the local information of image by u-sing gray information normally, which have the characteristics for translation invariance. The color features consider the color of the image and focus on description of the overall image information, which have the characteristics for translation, rotation and scale invariability. The shape features describe the outline or region feature of the specific objectives and focus on description of single target. By analyzing the cloud image features of four different sky conditions, extraction algorithms are introduced in details. Using gray-level co-occurrence matrix and Tamura texture, color moment, and moment invariants, 21 characteristic parameters are extracted. Because of its high performance in solving complex issues, simplicity of implementation and low computational complexity, the K-Nearest Neighbor (KNN) classification algorithm is selected to process 21 characteristic parameters. 8 different K values and different features combination are used to recognize the 4 types of sky conditions. Classification experiments are conducted using single feature, combination of each two features, and all of these features together. The 7 experimental results demonstrate that the new scheme is feasible. And using texture features, color features and shape features together can get better performance than using these features alone or any two of them combined. When the parameter K is set to 7 and all 21 characteristic parameters are considered, the identification accuracy of cumulus, stratus, cirrus and clear sky are 91. 1%, 74. 4%, 70. 0% and 100. 0%, respectively, with the average accuracy up to 83. 9%.
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