Open Access

ARTICLE

A Hypergraph-Embedded Convolutional Neural Network for Ice Crystal Particle Habit Classification

Mengyuan Liao¹, Jing Duan^2,3,*, Rong Zhang^2,3, Xu Zhou^2,3, Xi Wu¹, Xin Wang⁴, Jinrong Hu¹

1 School of Computer Science, Chengdu University of Information Technology, Chengdu, 610000, China
2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
3 Key Laboratory for Cloud Physics of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
4 Keya Medical, Seattle, WA, 98104, USA

* Corresponding Author: Jing Duan. Email:

Intelligent Automation & Soft Computing 2021, 29(3), 787-801. https://doi.org/10.32604/iasc.2021.018190

Received 28 February 2021; Accepted 02 April 2021; Issue published 01 July 2021

Abstract

In the field of weather modification, it is important to accurately identify the ice crystal particles in ice clouds. When ice crystal habits are correctly identified, cloud structure can be further understood and cloud seeding and other methods of weather modification can be used to change the microstructure of the cloud. Consequently, weather phenomena can be changed at an appropriate time to support human production and quality of life. However, ice crystal morphology is varied. Traditional ice crystal particle classification methods are based on expert experience, which is subjective and unreliable for the identification of the categories by threshold setting. In addition, existing deep learning methods are faced with the problem of improving classification performance on datasets with unbalanced sample distributions. Therefore, we designed a Convolutional Neural Network (CNN) embedded with a hypergraph convolution module, named Hy-INet. The hypergraph convolution module can effectively capture information from hypergraphs constructed from local and global feature spaces and learn the features of small samples in ice crystal datasets that have unbalanced sample numbers. Experimental results demonstrate that the proposed method can achieve superior performance in the classification task of ice crystal particle habits.

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Keywords

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