# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
###### Abstract
In 2017, [PERSON] [11] proposed a method for visibility restoration based on the fast single image defogging technique and a more robust atmospheric scattering model (ASM) which can overcome the problems of illumination nonuniformity and multiple scattering. [PERSON] [12] proposed a regression prediction model for the visibility forecast in the Urumqi International Airport. This regression prediction model was based on multi-factor, and its prediction result was very stable. When the visibility was higher than 1500 m, the average absolute error was better than 2000 m, and the prediction effect was less than 1000 m. [PERSON] [13] proposed an intelligent digital method to estimate the visibility by using the webcam weather images and the generalized regression neural network (GRNN). This proposed method uses a convolutional neural networks (CNN) network to estimate the visibility value of the webcam image through the pre-trained AlexNet. In the proposed model, the convolutional neural networks (CNN) network is used to extract image features, and the designed generalized regression neural network (GRNN) is used to approximate the visibility function with image features as input. However, the model visibility evaluation range is relatively limited (0-35 km), the training rate is 77.9%, and the test accuracy is about 61.8% only. [PERSON] [14] proposed a novel radial basis function (RBF) neural network method for haze elimination. One of the advantages of this method is that it can retain the edge of the visible structure and the brightness of image with the haze eliminated. This method can distinguish the haze component from the real-world haze images, and it can learn edge features according to the scene structure in the hidden layer of the radial basis function (RBF) network. This method can restore blurred images efficiently. In 2018, [PERSON] [15] proposed a novel deep learning method which involves feature extraction and uses of support vector machine (SVM) that can achieve safer driving conditions under foggy weather. This proposed method could be integrated into the next-generation variable information signs and the advanced driver assistance systems (ADAS) to alert the driver of the visibility range and recommend the appropriate speed,thereby helping to achieve safer driving in foggy condition. [PERSON] [16] proposed a novel deep hybrid convolutional neural network (DHCNN) method for visibility estimation under heavy foggy conditions. The proposed method used the Laplacian of the Gaussian filter to estimate the visibility of the image under low visibility (foggy) conditions. This method could replace high-cost visibility measuring instruments. The proposed method can estimate the visibility and collecting images through closed-circuit television in real time. [PERSON] [17] propose a deep learning method for estimating the relative atmospheric visibility from the digital images. The proposed method uses a shortcut connection to bridge a CNN module, which captures global view of an image, with a RNN coarse-to-fine module, which captures the farthest discerned local region. Although the evaluation capability of this CNN-RNN model was only 300-800 m, its accuracy could reach 90.3% accuracy. [PERSON] [18] proposed a novel method with the uses of Closed-circuit television (CCTV), to estimate the visibilities from digital images with sea fog. Due to the lack of effective information from the CCTV images over a long distance, the accuracy of this method is about 70%. In addition, the optical sensor is 4.5 km away from the installation point of the CCTV and this will cause some noise errors. In 2019, [PERSON] [19] proposed a multi-scale convolutional neural networks method for single-image dehazing. [PERSON] [20] proposed a method with hierarchical sparse representations to estimate the image visibility. The proposed method used the Fuzzy C-means algorithm (FCM) to build a historical database of 5000 samples, and uses a hierarchical sparse representation to predict the visibility of new inputs.