## 4 Conclusions  
There are many past research on the estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. Most of the past research has not focused effective feature and subregion extraction. This paper proposes a modified PSO method for effective feature extraction and visibilities estimation. Effective subregions are determined by grey averaging and adaptive threshold segmentation method. The selection of feature values at the output layer of ANN is optimized by PSO. The feature vectors are then imported to the SVR models and the overall visibilities is found by apply fusion method on the visibilities estimates of each sub-regions. Furthermore, the grey weighted averaging can remove the interference of moving objects. Feature vectors are formed by the selecting subset of the output feature values of the ANN (Densest, ResNet_50, Vgg16 and Vgg19). Overall visibilities estimate is found by applying the fusion methods to the estimated visibilities from the subregions.  
The accuracy of visibility estimation depended on the number of landmark object available for the overall process. Such external factor is depended on the environment of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Visibility Range (km)** & \\ \hline
**Network** & **11-20** & **21-30** & **31-40** \\ \hline VGG-16 (\%) & 91.42 & 90.89 & 89.66 \\ VGG-19 (\%) & 91.67 & 90.93 & 89.21 \\ DenseNet (\%) & 94.34 & 93.41 & 91.23 \\ ResNet\_50 (\%) & 94.71 & 93.91 & 91.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of different networks for different visibility ranges.  
the images collection site. This paper proposed a modification of the algorithms in [32] which could reduce the dimension of the feature states vector. Furthermore, we attempt to evaluate the effectiveness of the proposed algorithm by using a self-built visibility meter and digital camera setup at the selected campus site. This paper summarizes the outcomes of the experimental evaluation of a PSO based transfer learning method for meteorological visibility estimation method. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide the information for automatic extraction of effective regions from images. Effective subregions are extracted from image database and the image features are then extracted from the pre-trained Neural Network. Subset of Image features at output layer of ANN are selected based on the Particle Swarming Optimization (PSO) methods and the image feature vectors for each effective sub-region are generated. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and accurate.  
W.L.L.: conceived and designed the algorithms, analyzed the data, raised funding, managed projects, write articles and draft original manuscripts. H.F.: reviewed the manuscript. H.S.H.C.: analyzed the data and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received external funding from Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No.: UGC/FDS13/E02/18).  
Not applicable.  
The data are not publicly available as restricted data.  
The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No.: UGC/FDS13/E02/18).  
The authors declare no conflict of interest.