基于室外图像的天气现象识别方法

为提高室外视频监控的准确率，实现天气现象的自动观测，提出了一种基于室外图像的天气现象识别方法，该方法通过分析天气现象对图像的影响，提取图像功率谱斜率、对比度、噪声和饱和度等特征进行训练与分类，在训练过程中根据类别之间的特征距离建立分类决策树，并为决策树上非叶子节点构造支持向量机（SVM）分类器，并在每个分类器构造过程中通过对特征赋权值实现对特征的选择。通过对WILD图像数据库和采集图像集不同天气800个样本的测试，除了对降雨的识别率较低(75%)外，对晴、阴、雾天气的识别率均高于85%。

Method of weather recognition based on decision-tree-based SVM

To improve the quality of video surveillance outdoors and to automatically acquire the weather situations, a method to recognize weather situations in outdoor images is presented. It extracted such parameters as power spectrum slope, contrast, noise, saturation as features to realize the multi-classification of weather situations with Support Vector Machine (SVM). Then a decision tree was constructed in accordance with the distance between these features. The experimental results on WILD image base and our image set of eight hundred samples show that the proposed method can recognize sunny, overcast, foggy weather more than 85%, and recognize rainy weather more than 75%.