# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 3 Experiment Results and Analysis
### Result and Analysis
The main reason was that the larger the number of sub-regions, the higher the authenticity of the details in each effective sub-region, and the easier it was to be close to the true value. In addition, if the segmented single effective subregion was too large in scope, it had too many different levels of structure and details in that subregion, which would result in reduced sensitivity of the extracted features and thus reduced accuracy of the final detection. Likewise, if the extent of a single subregion was too small, the area containing too little hierarchical structure and detail would have limited validity and would not be as well differentiated in terms of visibility. All in all, according to the experimental results, it was more appropriate to divide the whole image into five subregions for experimental case in this paper. Figure 10: Example images of five subregions at different visibility range. (2)  
Performance of different feature extraction networks  
In order to verify the effectiveness of the proposed method in this paper, we have used different image features extraction networks, such as (VGG)-16 network, (VGG)-19 network, DenseNet network, and ResNet-50 network. The 512-dimensional feature vectors were extracted from the VGG-16 and VGG-19 networks. We extract the 1920-dimensional feature vectors from the DenseNet network. Feature vectors with 2048 entries were extracted from the ResNet_50 network as the coded features. Table 6 showed the visibility accuracy of these four networks in each visibility range. Although the overall accuracies of VGG-16 and VGG-19 network was 88%, the VGG-16 and VGG-19 networks gave lower accuracies in lower visibility range as compared with the DenseNet network and the ResNet-50 network. As ResNet_50 and DenseNet networks were more sensitive to image attenuation and could provide valid image features at different visibility levels, it could increase the network's extraction rate of valid features. These two networks would be more sensitive to provide valid features for visibility estimation regression. According to the experimental results, ResNet_50 network was recommended for image feature extraction, especially in the low visibility range. ResNet_50 network also had higher stability and robustness in other ranges. (3)  
Different fusion method  
In order to assess the visibility estimation results for different fusion strategies, we have evaluated the following fusion strategies, namely the random fusion, the average fusion and the proposed weight fusion. In the random fusion method, any one effective subregion after image segmentation was selected randomly for the feature extraction and regression model analysis, and its visibility result was regarded as the final fusion result. In the average fusion method, the average value of all the estimates from different subregions was used as the final fusion result. In the weight fusion method, the fusion weight of each effective subregion was derived from the results of the error analysis. The accuracies of different fusion methods were shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Deep Learning Network**} & \multicolumn{6}{c}{**Visibility Range (km)**} \\ \cline{2-7}  & **0–10** & **11–20** & **21–30** & **31–40** & **41–50** & **Total** \\ \hline VGG-16 (\%) & 84.36 & 91.31 & 89.16 & 87.45 & 87.22 & 88.26 \\ VGG-19 (\%) & 85.61 & 91.66 & 89.32 & 87.96 & 87.52 & 88.32 \\ DenseNet (\%) & 89.41 & 93.22 & 92.31 & 88.51 & 88.10 & 90.52 \\ ResNet\_50 (\%) & 89.11 & 94.58 & 91.51 & 88.62 & 88.33 & 91.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Visibility accuracy of different networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**No. of Effective Subregion**} & **No.1** & **No.2** & **No.3** & **No.4** & **No.5** \\ \hline Estimated visibility & 16.44 & 13.77 & 15.81 & 14.97 & 13.07 \\ Fusion Weight & 0.12 & 0.23 & 0.25 & 0.18 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The estimated results of different effective area.