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

Abstract

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust.

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

“…An SVM is a classic machine learning classification model and is fully supported by mathematical principles. It has still been widely adopted in recent years [30,31,33,35]. Based on the supervised statistical learning theory, an SVM can effectively classify highdimensional data such as images.…”

“…Giyenko et al [32] applied convolutional neural networks (CNN) to classify the visibility with a step of 1000 m, and their model achieved an accuracy of around 84% when using CCTV images. Lo et al [33] established a multiple support vector regression model with an estimation accuracy above 90% regarding the visibilities in high-visibility conditions. You et al [34] proposed a CNN-RNN (recurrent neural network) coarse-to-fine model to estimate the relative atmospheric visibility through deep learning by using massive pictures from the Internet.…”

Visibility estimation via deep label distribution learning in cloud environment

“…Li et al [25] propose a transfer learning method based on the feature fusion for visibility estimation. Lo et al [26] further introduce PSO feature selection into the transfer learning method to improve the performance. These methods all treat the visibility estimation problem as a regression model, which is suffered from the label ambiguity challenges.…”

Visibility Estimation Based on Weakly Supervised Learning under Discrete Label Distribution

“…In foggy images, the distribution of fog is non-uniform, with more texture features present in the foreground of the image, while the distant regions mostly contain fog-related information, lacking actual scene texture features. To address the problem of visibility estimation more effectively, Wai Lun Lo et al [15,16] and Jiaping Li et al [17] choose the manual extraction of regions of interest (ROI), and Yang You et al [18] proposed a relative CNN-RNN model, where a CNN is used to capture global information and a recurrent neural network (RNN) is used to search for the farthest area in the image. In the field of deep learning, many methods have been proposed to enhance image features and improve model robustness.…”