# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 3 Experiment Results and Analysis
### Result and Analysis
\begin{table}
\begin{tabular}{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 5: Visibility accuracy of fusion of effective subregion. According to the results of Table 7, random selection strategy give the poorest results. As the randomly selected method has not included sufficient feature values for visibility estimation. In random fusion method, only the features of the selected effective sub-region are used, which reduces the information for visibility evaluation, thereby affecting the final accuracy. Factors such as the uneven light illumination in the image may cause excessive errors in final estimated visibility value. Compared with the random fusion method, the average fusion method could give better performances in the range of 21-50 km as compared to the random fusion method. The performance of average fusion method in low visibility range (0-20 km) is still not satisfactory. On the other hand, the weight fusion method effectively fused the local estimates of the subregion images with the considerations of the fitted variances of the predicted distribution. Therefore, weight fusion can give better robustness and stability. In summary, weighted fusion method can give the best visibility estimation results for whole visibility range. (4) Comparison of different methods in other paper  
As compared with the method in [25], the accuracy of visibility estimation in [25] was only about 80%. The effective subregions in [25] are selected based on pre-requisite landmark objects information and human judgement. The selection process is not from an objective approach of regression model analysis. Therefore, some important image information could be ignored after the subregion selection process. Furthermore, as only one single subregion is used for the visibility estimation by support vector machine (SVM), the accuracy is about 82% only. On the other hand, the accuracy of the proposed method in this paper can be about 90%. It shows that the effective subregions selection method proposed in this paper was more reasonable and the fusion method can give more accurate results. As compared with the method in [21], the accuracy of visibility estimation in [21] can also reach 90%. However, the sub-regions are extracted based on equal division of image. Subregions' selection by equal division of the whole image may have the following disadvantages. Useful image objects and the reductant image objects will be mixed and distribute among different subregions. As the proposed algorithm in [21] adjust the subregions image to the size of 224 \(\times\) 3 \(\times\) 224 after the division process, this will cause deformation of the subregion image and affect the estimation results. As the useful image information in the subregions is not extracted efficiently, this will increase the computation time. The proposed method in this paper focus on more effective selection of subregions with useful static objects, it can reduce the area with reductant information in the subregions. The proposed method solves the problem of low data processing efficiency and low estimation accuracy, it can reduce the unnecessary computation load for the reductant image information in the image. This proposed method can extract the effective subregions effectively and it can also provide reasonable accuracy for a wide estimation range with the uses of multiple SVR models and fusion method. The mapping surface between the visibility values and the feature vectors is complex and has high dimensions. By incorporating a number of piecewise SVR models, the multiple SVR models can approximate the highly dimension complex mapping surface of the visibility function.