We are pleased to announce the publication of a journal article titled “Meteorological Visibility Estimation Using Landmark Object Extraction and the ANN Method” in Sensors, Volume 25, Issue 3, Article 951 (February 2025). The paper was led by Professor Wai-Lun Lo of our institution as the first author, in collaboration with City University of Hong Kong and The Education University of Hong Kong.
- Wai-Lun Lo, Kwok-Wai Wong, Tai-Chiu Hsung, Shu-Hung Chung and Hong Fu, “Meteorological Visibility Estimation by Using Landmark Objects Extraction and ANN Method”, Sensors, 25(3), 951, 2025.
Abstract
Visibility can be interpreted as the largest distance of an object that can be recognized or detected under a bright environment that can be used as an environmental indicator for weather conditions and air pollution. The accuracy of the classical approach of visibility calculation, in which meteorological laws and image feature extraction from digital images are used, depends on the quality and noise disturbances of the image. Therefore, artificial intelligence (AI) and digital image approaches have been proposed for visibility estimation in the past. Image features for the whole digital image are generated by pre-trained convolutional neural networks, and the Artificial Neural Network (ANN) is designed for correlation between image features and visibilities. Instead of using the information of the whole digital images, past research has been proposed to identify effective subregions from which image features are generated. A generalized regression neural network (GRNN) was designed to correlate the image features with the visibilities. Past research results showed that this method is more accurate than the classical approach of using handcrafted features. However, the selection of effective subregions of digital images is not fully automated and is based on manual selection by expert judgments. In this paper, we proposed an automatic effective subregion selection method using landmark object extraction techniques. Image features are generated from these LMO subregions, and the ANN is designed to approximate the mapping between LMO regions’ feature values and visibility values. The experimental results show that this approach can minimize the reductant information for ANN training and improve the accuracy of visibility estimation as compared to the single image approach.
The research team members
Hong Kong Chu Hai College
- Professor Wai-Lun Lo (Professor and Head of the Department of Computer Science)
- Kwok-Wai Wong (Research Assistant, Department of Computer Science)
- Richard Tai-Chiu Hsung (Associate Professor, Department of Computer Science)
City University of Hong Kong
- Professor Henry Shu-Hung Chung (Chair Professor, Department of Electrical Engineering)
The Education University of Hong Kong
- Hong Fu (Associate Professor, Department of Mathematics and Information Technology)
Photos from the paper
The prediction results using different types of models
