## 2 Methodology  
### Related Work  
Some of the past research on deep neutral network methods for the visibility estimation study on the network optimization, network performances improvement, shortening the computation time [12; 13] and accuracy improvement. However, accuracy improvement may also increase the computation load [17]. Fusion methods for increasing the adaptability of the extracted features has been proposed [21] but reductant features would affect the training efficiency and estimation accuracy. This paper proposes a visibility estimation algorithm for effective features extraction and minimizing reductant information for meteorological visibility estimation. For the transfer learning approach, [PERSON] [13] proposed to use a Convolutional Neural Networks (CNN) network (AlexNet) to extract image features from low resolution Webcam images and the visibilities are estimated by using a Generalized Regression Neural Network (GRNN). However, the estimation is relatively narrow, and the estimation accuracy is about 61.8% only. In order to improve the estimation accuracy, past research has been accomplished for selecting image subregions based on prerequisite landmark target information and human judgement [25]. A single subregion is used in this the method and the overall accuracy is about 87%. In [13], the paper attempted to identify relevant feature from large number of extracted features from pre-trained Convolutional Neural Networks (CNN) model (AlexNet). However, test accuracy was very low (61.8%) and it gave high prediction error and reference objects were required for classification. The case using the whole image as reference has been considered in [13]. It has been found that subregions which focus on selected landmark objects could give better accuracy as compared to the prediction results obtained by the whole image. In [25], subregions images were divided into different classes according to visibility range. Features are imported into different SVM for visibility estimation [25]. However, the subregions are selected based on human judgement of landmark objects and some important image information or area may be ignored. The feature values at the output layer of the VGG16 network were used for SVM parameters estimation. The case of using the whole image without estimates' fusion as reference has also been considered in [25] in which the estimation accuracy was also about 87% but the method proposed in [32] could give accuracy up to 90%. In [17], model could be adapted for a small practical dataset with sparsely distributed visibilities, but it costed high computation load and consumed long time for training. The method employed manual annotations instead of sensors and the visibility assessment range is relatively narrow. In [24], [PERSON] has proposed to use a deep integrated convolutional neural networks (VisNet) method to estimate images visibility by using webcam weather images. The ANN training is quite time-consuming as it has a preprocessing stage and several integrated Convolutional Neural Networks CNN layers and the calculation time is relatively long. In order solve the problem of automatic subregion selection, a novel deep learning neural network method for the meteorological visibility estimation based on image feature fusion method has been proposed in [32], which can find the most effective image subregions through image pre-processing and gray-level averaging process. In [32], the pre-processing gray-weighed averaging was performed in the first step. The coordinates of the effective subregions were located. The subregions' image features are then extracted by the deep learning neural network (VGG-16 network [33], VGG-19 network [33], DenseNet network [34] and ResNet_50 network [35]). The subregions' regression models and visibility estimates were obtained by the Support Vector Machine (SVM) methods. The overall visibility estimates of the whole digital image was then obtained by the fusion method.