# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 3 Experiment Results and Analysis
### Result and Analysis  
In the experiment, the visibility of the test set images was evaluated according to the neural network-regression model. Through gray averaging and region segmentation, we had obtained the effective subregions. First, we performed the feature extraction on the extracted effective area, and then imported the extracted features into the support vector machine model for training. Finally, we obtained the predicted visibility value. Comparing the actual visibility value and the predicted visibility value in each effective subregion, we could get the visibility evaluation result of the effective subregions. By analyzing the error of each effective subregions, the fusion weight of each effective area can be obtained. The fusion weights were used for the fusion of the visibility evaluation model of all effective subregions. Finally, the comprehensive visibility of fusion could be obtained. The organization of the sections for the results and analysis are summarized as follows. Among them, for different effective subregions, the results of model analysis of the visibility evaluation were shown in part (1). In order to verify the effectiveness of the method proposed in this paper more extensively, we have used different image features extracted from different networks, such as (VGG)-16 network, (VGG)-19 network, DenseNet network, ResNet-50 network. The extracted features were imported into support vector machines to train, and finally we got the visibility regression results. In part (2), we evaluated the performances of the four networks, (VGG)-16 network, (VGG)-19 network, DenseNet network and ResNet-50 network. The detailed experimental results were shown in part (2). \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Configuration** \\ \hline Operating System & Linux \\ Memory Capacity & 32 GB memory \\ Central Processing Unit & 2.6 GHz Intel CPU i7-8700 \\ Graphics Processing Unit & NVIDIA GeForce GTX 1060 Ti \\ Software platform & Python 3.6 \\ Deep Learning Library & Keras \\ Image database & Hong Kong Observatory (HKO) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The hardware and software parameters of the server. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image Database of Hong Kong** & \multicolumn{5}{c}{**Visibility Range (km)**} \\ \cline{2-7}
**Observatory** & **0–10** & **11–20** & **21–30** & **31–40** & **40–50** & **Total** \\ \hline No. of training set sample images & 432 & 1271 & 762 & 726 & 439 & 3630 \\ No. of test set sample images & 144 & 424 & 254 & 242 & 146 & 1210 \\ Total & 576 & 1695 & 1016 & 968 & 585 & 4841 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Visibilities distribution of the image database. In addition, in order to verify the performance of the method in different visibility ranges, we have evaluated the results for different visibility ranges in part (2). Finally, in order to compare the fusion methods, the experimental results under different fusion strategies were analyzed and discussed in part (3). In part (4), we compared the fusion effect of this paper with those in other papers. (1) Analysis of different effective areas  
In order to assess the validity of the effective subregions, the visibility estimates and the subregion weights of each effective subregion were shown in Table 4. The visibility of subregions with detailed objects, such as effective subregion No.3, No.4, and No.5 in Figure 10, was closer to the actual visibility. These subregions with rich details were weighted correspondingly higher than the blurred subregions. In order to assess the effect of the number of effective subregions on the estimation accuracy, the accuracies with different numbers of effective subregions were shown in Table 5. The accuracy with the fusion of one effective subregion (No.1) and three effective subregions (No.1, No.2, and No.3) were much lower than that obtained by fusion of all five subregions (No.1-No.5).