# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Evaluation Range (m)** & & **Advantages** & & **Disadvantages** \\ \hline The pre-trained Convolutional Neural Networks (CNN) model (AlexNet) was used to perform feature extraction on the image, and the proposed Generalized Regression Neural Network (GNN) is used for the evaluation of video visibility. Finally, visibility of we mean weather images was classified [13]. & 1. & Visibility evaluation range of the image data set was larger than other methods. & 1. & Uses a relatively small practical dataset with uneven distribution Test accuracy is very low (61.8\%). \\ \hline Quickly bridge Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Use CNN-RNN for visibility learning, and relative Support Vector Machine (SVR) for regression analysis [17]. & 300-800 & 2. & Results too high prediction error (13.8\%). \\ \hline Using a novel deep integrated convolutional neural networks (VisNet) method to estimate images visibly using web stream weather images. Three deeply integrated convolutional neural network streams were connected in parallel in the VisNet. & 1. & Proposed VisNet was more robust and could be used as a universal visibility estimator. & 1. & Relatively long calculation time. Network had a preprocessing stage and several integrated Convolutional Neural Networks CNN layers, network training is quite time-consuming. & 1. & Relatively long calculation time. Network had a preprocessing stage and several integrated Convolutional Neural Networks CNN layers, network training is quite time-consuming. & 1. & Relatively long calculation time. Network had a preprocessing stage and several integrated Convolutional Neural Networks CNN layers, network training is quite time-consuming. & Network’s CNN layers, network training is quite time-consuming. & Network’s CNN layers, network training is quite time-consuming. & network training is quite time-consuming. \\ \hline \multirow{2}{*}{2.5} & \multirow{2}{*}{Proposed model could only be applied to day-time images, at datasets can be processed by simply adjusting the iterative steps.} & 2. & Proposed model could only be applied to day-time images, different methods are needed to solve this problem. & 2. & Proposed model could only be applied to day-time images, different methods are trained to day-time images, different methods are needed to solve this problem. & 2. & Proposed model could only be applied to day-time images, different methods are trained to day-time images, different methods are needed to solve this problem. & 2. & Annotationally costly and consumes long time to train. \\ \hline \multirow{2}{*}{3.5} & \multirow{2}{*}{Proposed method did not require to define a precise physical model features from the image. Using deep convolutional neural networks (DCNN) for transfer learning. Using Support Vector Regression (SVR) and fusion to estimate visibility [21].} & 1. & The proposed method did not require to define a precise physical model features from the image. Using deep convolutional neural networks (FEV-V network) features from the image. Using deep convolutional neural networks (DCNN) for transfer learning. Using Support Vector Regression (SVR) and fusion to estimate visibility [21]. & 1. & The proposed method did not require to define a precise physical model features from the image. Using deep convolutional neural networks (CNN) for transfer learning. Using Support Vector Regression (SVR) and fusion to estimate visibility [21]. & 1. & The proposed method is based on a dataset with about 1000 images and the subregions are selected based on human judgement of landmark objects. & Some important image information or area may be ignored. & 2. & Features of the extracted landmark sub-regions were fused, only a single sub-region was used for regression analysis and visibility estimation, which leads to some error in estimation results. & 2. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of some proposed methods.