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.

Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

“…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 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.…”

“…However, in [32], all the image feature generated by the pre-trained Artificial Neural Network (VGG-16, VGG-19, DenseNet and ResNet_50 network) are used for the next stage of SVM modelling. Large feature vector's dimension will increase the computation load significantly, especially in matrix calculations.…”

“…It is expected that not all the image features at the output layer of the ANN could provide effective information for the estimation of visibilities. Therefore, this paper proposed a modification of the algorithms in [32] which could reduce the dimension of the feature vector. The evaluation in [32] was based on a dataset provided by the Hong Kong Observatory (HKO) which measured the visibilities by the sensors and camera installed at fixed locations selected by the HKO.…”

“…Therefore, this paper proposed a modification of the algorithms in [32] which could reduce the dimension of the feature vector. The evaluation in [32] was based on a dataset provided by the Hong Kong Observatory (HKO) which measured the visibilities by the sensors and camera installed at fixed locations selected by the HKO. In this paper, we attempt to evaluate the effectiveness of the proposed method by using a self-built visibility monitoring system setup at the selected campus site.…”