# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
###### Abstract  
The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoid clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation of 93%, 6.4% higher than the RF model without data expansion, 3-6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution. fog density; data augmentation; random forest; generative adversarial network; hierarchical clustering; fog-relevant features +
Footnote †: journal: Medical  
automated instruments such as transmissometers and scatterometers [4; 5] are employed to observe horizontal visibility, which is derived from atmospheric optical processes. However, automatic observation systems often suffer from limited representativeness when local optical characteristics differ from those of the broader environment. Additionally, large-scale and high-density deployments of these systems are prohibitively expensive. Satellite-based fog remote sensing also faces challenges, particularly due to cloud interference, which reduces accuracy [6]. In recent years, the widespread use of surveillance cameras across various industries has sparked research into optical recognition, image denoising, and fog visibility observation, becoming a focal point in the fields of image recognition and artificial intelligence [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Currently, fog density estimation based on optical images can be divided into two categories: traditional computer vision methods and neural network-based approaches. Traditional computer vision methods estimate visibility using image processing techniques, extracting features such as Region of Interest (ROI) extraction, edge detection, vanishing points, and horizon detection, and applying linear statistical equations to estimate fog visibility. Among these methods, [PERSON] et al. [17] introduced a visibility estimation method using B-spline wavelet transforms. [PERSON] et al. [18] calculated fog visibility distance by extracting road and sky regions. [PERSON] et al. [19] proposed a method for detecting fog from moving vehicles by analyzing inflection points and the horizon in the images. [PERSON] et al. [20] developed a visibility estimation technique using ROI extraction and camera parameter estimation. [PERSON] et al. [21] presented a series of techniques for fog visibility estimation, including edge detection, contrast reduction between consecutive images, decision tree methods, and linear regression models. [PERSON] et al. [22] introduced an algorithm for visibility estimation under dark, snowy, and foggy conditions, combining dark channel prior, support vector machines, and weighted image entropy. [PERSON] et al. [23] proposed an improved visibility estimation algorithm using piecewise stationary time series analysis and image entropy, incorporating subjective assessments to judge fog and haze visibility. [PERSON] et al. [24] estimated fog density in weather images by analyzing saturation and brightness in the HSV color space. Despite the effectiveness of these image feature-based methods, they face challenges such as limited generalization ability and low flexibility.