基于SVM和决策树的自然图像低能见度天气现象识别

Low Visibility Weather Recognition via SVM and Decision Tree in Single Image

摘要: 一个地区的能见度不但反映了该地区大气环境的质量，并且与人们的生活有着密切相关的联系。通常，低能见度天气也严重地影响了人们的经济生产，因此其观测具有重要意义。大气环境能见度较低的原因与气象条件有着密切的关联，低能见度的天气现象主要有雨、雪、雾霾、沙尘等。本文提出了一种基于室外单幅自然图像的低能见度天气现象识别算法，该算法通过低能见度天气现象对图像光学信息的影响，提取图像的对比度、饱和度、亮度等特征参数信息进行训练和分类，在训练过程中根据各类别特征之间的距离建立分类决策树，并为决策树构建支持向量机(SVM)分类器，对低能见度天气进行自动分类识别。通过对互联网上的大量低能见度天气光学图像的训练和测试，算法对低能见度的天气现象的平均识别率可达70%。该算法可以为分布式识别提供技术支持，然后采用分布式识别投票，最终可以把识别正确率提高到95%以上。

Abstract: The visibility in a region not only reflects the quality of the atmospheric environment, but also has close relationship with people’s life. In general, low visibility weather affects people’s economic development, so the real-time observation of low visibility is of much signification. The reason of low visibility is closely associated with meteorological conditions. The low visibility weather phenomena mainly contain rain, snow, fog, etc. This paper proposes a recognition method which is based on low visibility weather phenomenon by means of the influence of low visibility weather phenomenon on the image information such as the image contrast, saturation and brightness that can be employed for training and classification. We establish a classification decision tree according to the distance between the different categories in the process of training and building support vector machine (SVM) classifier for the decision tree. It can classify the low visibility weather image automatically and intelligently. Through testing a huge amount of images downloaded from the internet, the experimental results show that weather image mean recognition rate is over 70%. After adopting the voting scheme via distributed recognition, the final low visibility weather recognition rate is more than 95%.