# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 4 Discussion and Summary  
According to the results in Figure 10 and Section 3.2. It can be found that  
1. Each effective subregion \(R_{i}\) contains some static landmark objects at certain distances from observer location. Suppose \(R_{i}\) contains a nearest static object at distance \(x_{i}\). If visibility is below \(x_{i}\), all the objects in \(R_{i}\) cannot be observed and the whole region will be appeared as a uniform gray region as outline edges of the objects cannot be seen in this visibility range. Variations of image characteristics (e.g., gray level, and image sharpness of static objects) are observable for visibility range greater \(x_{i}\) and \(R_{i}\) can provide useful image feature information for visibility estimation in a visibility range above \(x_{i}\). 2. The weather images in this paper were collected in order of time sequence. Taking the hull as an example, the same hull will appear in different positions in the images. The entire image sequence records the moving trajectory of the hull from appearance to departure. Theoretically, a dynamic object must have a mean position from entering to departure. So long as the moving objects does not remain stationary at a certain position for a long time. These objects usually could be removed after the gray averaging process. 3. The proposed method is even more effective for removing the natural moving objects in the nature such as moving clouds or sea waves. The area for the sky or the sea will become a uniform gray region after the gray averaging process. Therefore, after carrying out the proposed gray level averaging process, dynamic objects and natural moving objects could be removed in the image, leaving only the static objects in the image for subsequent region selection and feature extraction. 4. In this case, if we perform the gray level averaging for the image dataset with visibilities higher than \(x_{i}\). All static objects at distance less than \(x_{i}\) in \(R_{i}\) will be appeared as clear objects with sharp outline edges. Furthermore, dynamic objects (e.g., moving cloud and sea) will be filtered out if the total number of images is sufficiently large. 5. In summary, performing gray level average on the entire image database could find static objects observable in different visibility ranges and locate the coordinates of the effective sub-regions. Combining with the threshold segmentation method, the gray level averaging can be used to detect observable static objects for different visibility ranges. 6. The Feature Extraction and Regression model proposed in this paper is based on effective subregions selection, feature extraction by deep learning neural network, multiple support vector regression (SVR) models and weight fusion model for visibility estimation. Each support vector regression (SVR) model provide piecewise approximation for the overall complex mapping between visibility and image features. 7. Different from other fusion methods as proposed in [21], the fusion in this paper aims to exclude invalid areas, reduce the amount of calculation and maintain the accuracy of visibility evaluation. Actually, the fusion approach in [21] does not exclude invalid regions, while the proposed method in this paper does not restrict the size and location of the subregions. It determines the segmentation of image according to the content of static objects in the whole image. 8. In case the image contains no static objects in a particular visibility distance, visibility value at this range is estimated based on the interpolation (or fusion) of visibility values from other subregions. 9. In summary, the proposed method can reduce the processing time of image features as only the selected regions are analyzed instead of the whole image. It can improve the estimation accuracy by using fusion for the estimates from different subregions. The proposed method shows good performance and results for the practical visibility data provided by HKO. Experimental results show that the accuracy of visibility estimation reaches more than 90%.