# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
###### Abstract  
Meteorological visibility is an important meteorological observation indicator to measure the weather transparency which is important for the transport safety. It is a challenging problem to estimate the visibilities accurately from the image characteristics. This paper proposes a transfer learning method for the meteorological visibility estimation based on image feature fusion. Different from the existing methods, the proposed method estimates the visibility based on the data processing and features' extraction in the selected subregions of the whole image and therefore it had less computation load and higher efficiency. All the database images were gray-averaged firstly for the selection of effective subregions and features extraction. Effective subregions are extracted for static landmark objects which can provide useful information for visibility estimation. Four different feature extraction methods (Densest, ResNet50, Vgg16, and Vgg19) were used for the feature extraction of the subregions. The features extracted by the neural network were then imported into the proposed support vector regression (SVR) regression model, which derives the estimated visibilities of the subregions. Finally, based on the weight fusion of the visibility estimates from the subregion models, an overall comprehensive visibility was estimated for the whole image. Experimental results show that the visibility estimation accuracy is more than 90%. This method can estimate the visibility of the image, with high robustness and effectiveness. v 2021 22 January 2021 22 January 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021  
environment to be measured and the personal subjective judgement of the weather observer [6]. Furthermore, this method is inefficient and irreproducible, and the observation of visibility is also limited by the time between the observations and the environmental changes. The visibility meter approach includes the forward scattering method and the back-scattering method [7]. In general, based on the cost and performance considerations, most of visibility meters use the forward scattering method. However, accurate forward scattering equipment is very expensive and requires specialized installation and calibration skills. Furthermore, it can only measure the visibilities accurately within a relatively short visible range. In recent years, due to the continuous advancement of computers and digital cameras, the digital images obtained by web cameras could be used for computer vision and obtaining accurate scene information. Many past researches have been done on visibility estimation and visibility restoration under low visibility conditions by studying the blurred and degrade images obtained by digital camera. In 2016, [PERSON] et al. [8] presented a new approach with three modules, depth estimation, color analysis, and visibility restoration to solve the problem of visibility restoration of outdoor digital images in presence of haze, fog, and sandstorms. This method could simplify the complex image restoration problem. Compared with other methods, this method can be applied to images in different weather conditions, it is quick and efficient for images restoration with the removal of fog. [PERSON] [9] proposed a novel deep neural networks approach for the visibility enhancement under low visibility in foggy conditions. They proposed a generalized model, with an approximate model generated by the deep learning neural network for the fog in the scene, to restore the image quality of the scene. This method could restore the scene of the image in real time without other prerequisite information. [PERSON] [10] proposed a deep network that can recognize the local patch and the three-color channels information to enhance the image quality by dehazing process.