关键词: 卷积神经网络, Gabor滤波, 浓雾天气形势图, 识别分类

Abstract: Correct classification of fog weather situation map is one of key factors to carry out a fog intelligent online recognition model, in order to efficiently and correctly recognize fog type（ToF） according to Weather Situation Map（WSM）, a Gabor-Convolutional Neural Network（G-CNN） fog intelligent online recognition model is proposed, within training built WSM-ToF relation dataset through G-CNN to build a non-linear map relationship between WSM texture feature and the classification label based on mankind forecasting. In theory, Gabor filter is firstly applied to enhance texture feature of input image by G-CNN. Secondly, in order to build a non-linear map relationship between the input and the output, it adopts G-CNN architecture with 6-layers to carry out non-linear map relationship, in which output layer is taken as the predicted fog label for input. With WSM-ToF relation dataset of Jiangsu region from 2010 to 2016, 70% samples are randomly taken as training dataset and the rest 30% is taken as test dataset, and the model is trained, tested and evaluated based on the three common indicators：Probability of Detection（POD）, False Alarm Rate（FAR） and Critical Success Index（CSI）, experimental results show that POD, FAR and CSI are reached at 0.86, 0.11 and 0.77, and these evaluation values comfirm the proposed model is highly correct and efficient.

Key words: convolutional neural network, Gabor filtering, fog weather situation map, identification classification