# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
###### Abstract
This hierarchical sparse representation method is easy to expand, which could improve accuracy, reduce absolute errors, and provide convenience for other meteorological analysis. [PERSON] [21] proposed a novel deep convolutional neural networks (DCNN) method for visibility estimation under the condition of insufficient visibility labeled data. This proposed method divided each image into several sub-regions, used a neural network without reference image to extract features from the image. Then the extracted features were imported into support vector regression for training, and the visibility evaluation of each sub-region were obtained. The final visibility evaluation was obtained according to the fusion weight of the regression model. The results of the proposed method showed that the accuracy of visibility estimation can be more than 90%. [PERSON] [22] proposed a novel method based on \"learning features\" to estimate the visibility under foggy weather, in which AlexNet deep convolutional neural networks (DCNN) was used for feature extraction, and support vector machine (SVM) classifier was used for visibility estimation. [PERSON] [23] presented a visibility prediction method based on the multimodal fusion. The proposer method established the numerical prediction model with XGBoost, LightGBM, and emission detection algorithms. [PERSON] [24] gave a detailed overview of the latest research results on visibility estimation under various weather conditions. He proposed a novel deep integrated convolutional neural networks (VisNet) method to estimate images visibility by using webcam weather images and three deeply integrated convolutional neural network streams are connected in parallel in the VisNet. Compared with other methods, the proposed VisNet network had more advantages in versatility. However, the proposed method involves quite heavy computation and extensive data processing. In 2020, [PERSON] [25] proposed a novel multiple support vector regression (MSVR) model for visibility estimation. This proposed method extracted different subregion areas from the weather images according to the prescribed landmarks' information and used the VGG16 network to extract image features. According to different visibility ranges, the images are divided into different classes and their features were imported into the Support Vector Machine (SVM) for regression analysis and visibility estimation. However, the method only uses a single subregion for visibility estimation and the overall accuracy of this method was about 87% only. The comparisons of some proposed methods are summarized in Table 1.  
At present, deep neural networks had been widely used in the visibility estimation and restoration of weather images. Past research used various forms of neural networks to extract features from digital images, and used the extracted features as input data for classification and evaluation. Some methods focused on network optimization, network performances and shortening the computation time [12; 13], while some methods focused on the improving the accuracy of the visibility estimation., Some of these past researches focused on better accuracy in smaller estimation range but these methods increase the computation load [17]. Past research work has been done for improving the efficiency of the algorithm by using fusion methods so as to increase the adaptability of the extracted features [21]. However, as some of the extracted features were reductant, these features would affect the training efficiency and the accuracy of the estimation results. From the perspective of features extraction, this paper looked for effective features extraction and reducing reductant image information for meteorological visibility estimation.