Low Visibility Weather Recognition via SVM and Decision Tree in Single Image
Guanlei Xu, Xiaotong Wang, Limin Shao, Lijia Zhou, Xiaogang Xu · 2016
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%.