# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 5 Conclusions  
At present, deep neural networks have been widely used in the visibility estimation of weather images, but most methods do not focus on the features and content of the effective subregions. From the perspective of effective feature extraction, this paper looked for efficient selection of useful subregions and features extraction for the visibility estimation. This paper proposed a novel deep learning neural network method for the visibility estimation based on feature fusion method, which located the most effective image subregions by gray-level averaging. This proposed method used deep learning neural network to extract features and the established visibility models for each subregion by using Support Vector Machine (SVM). The visibilities of the sub-regions were fused together according to the results of the weight fusion analysis. In the proposed method, all the images in the database were gray-weighted firstly to remove interference areas, in order to obtain the effective subregions. In this paper, five subregions were extracted for subsequent feature extraction. Four feature extraction networks (Densest, ResNet_50, Vgg16, and Vgg19) were used to extract features from the subregions. The features vectors obtained by the neural network were then imported into the proposed SVR regression models, in which the visibility functions with image features input are curve fitted by the SVR models. According to the results of the error analysis, the weight fusion was performed to derive the final visibility estimate. This proposed method extracts valid and effective subregions to improve the training efficiency and estimation accuracy, it also solves the problem of long computation time due to the data processing of the whole image or equally divided subregions of the whole image. Since the effective subregions were derived by the gray average method during pre-processing, this process in performed only in the initialization stage. While the image processing is performed only for the selected effective subregions, image processing for the invalid and reductant area is avoid. Compared with other methods, this paper not only extracted the features of image efficiently, it shortened the data processing time for the whole process, and it improved the efficiency of model training, and avoided the interference of invalid features. The major idea of the proposed subregions selection method is summarized as follows. As the distribution, number and location of static landmark objects in a digital image are depended on the actual physical environment, we cannot select the effective subregions arbitrarily. Suppose the image dataset are sorted in ascending order of visibility distances. We can identify the location of the nearest to farthest objects by performing the gray level average for the dataset from the smallest to the largest visibility distance. The major aim of the proposed selection method is to group a set of static landmark objects into a particular subregion so that the variation of its image characteristic is sensitive a particular visibility range. Hence, we can train a SVR model to curve fitted the visibility function with the input image features. By combining these SVR models by fusion method, we can estimate the final visibility by using the approximate multiple SVR model. The proposed method gives good performances and accuracies in a range of 0-50 km which is suitable for practical applications. Experimental results show that the visibility estimation accuracy of the proposed method is more than 90%. It could be used to estimate the visibility value of the whole image, with high robustness and effectiveness. This method does not require to define a large-scale visual annotation set, and it also eliminates the processing of invalid and reductant information on the digital images as compared to other existing methods. For the fine-tuning of the neural network and extraction of effective subregions, it greatly reduces the model complexity and the computation time as compared to other methods.