中文摘要：

大气系统中云的辐射特性以及分布情况决定了天气预报的准确性和气候监测的有效性。云的检测与识别对大气探测和大气遥感至关重要。本研究旨在通过提取可见光云图的纹理特征、颜色特征和sift特征自动训练分类器,实现对卷云、积云、层云和晴空的分类识别。本研究采用极限学习机（extreme learning machine）对样本进行学习,并在不同条件下进行云分类识别。实验结果表明：当纹理特征、颜色特征和sift特征融合在一起时,获得了比单独使用纹理特征、颜色特征和sift特征以及它们两两组合时更好的识别效果,识别正确率分别为87.67%、90.75%、74.50%和93.63%,平均正确率达到86.64%。在相同实验条件下,本文采用的方法比人工神经网络（artificial neural network）、K近邻（k-nearest neighbor）和支持向量机（support vector machine）好。

英文摘要：

Cloud radiation properties and distribution significantly determine the forecasting accuracy and the climate monitoring effectiveness. Cloud detection and recognition are crucial for atmospheric sounding and atmospheric remote sensing. The purpose of this study is to realize the classification of cirrus,cumulus,stratus and clear sky by means of extracting texture features,color features and sift features to automatically train the classifier. This paper uses the extreme learning machine to study the samples and does cloud-type classification and recognition under different experimental conditions. The experiment results show that using texture features,color features and sift features together get better performance than using these features alone or any two of them together,and the accurate identification rates of cirrus,cumulus,stratus and clear sky are 87. 67%,90. 75%,74. 50% and 93. 63%,respectively,with an average of86. 64%. Under the same experiment conditions,the proposed method can outperform the artificial neutral network（ ANN）,the k-nearest neighbor（ KNN） and the support vector machine（ SVM）.