A Novel Computer-Aided Cloud Type Classification Method Based on Convolutional Neural Network with Squeeze-And- Excitation

Autor:	Guanshen Li
Rok vydání:	2021
Předmět:	History Computer science business.industry Cloud computing Pattern recognition Type (model theory) Convolutional neural network Computer Science Applications Education Computer-aided Classification methods Artificial intelligence business Excitation
Zdroj:	Journal of Physics: Conference Series. 1802:032051
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1802/3/032051
Popis:	Clouds have a huge impact on the energy balance, climate and weather of the earth. Cloud types have different cloud radiation effects, which is an important indicator of cloud radiation effects. Therefore, determining the type of cloud is of great significance in meteorology. In this paper, the Convolutional neural network with Squeeze & Excitation Networks (SENet) are mainly used to solve this probelm. CNN can automatically learn the filters that need to be manually set before, and can learn complex edge, spatial and texture information in the image which are difficult for traditional methods to learn and extract. Moerover, a website and a deep learning framework are established to showcase the results of this article and to further develop our models and methods through open source methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3a175ce19d3807eba2881f7c26ea6694 https://doi.org/10.1088/1742-6596/1802/3/032051 Zobrazit plný text záznamu