Galaxy morphology classification based on ResNeXt

Yang Yu

doi:10.1117/12.2667464

28 March 2023 Galaxy morphology classification based on ResNeXt

Yang Yu

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125664Z (2023) https://doi.org/10.1117/12.2667464
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

The morphology of galaxies can reflect the physical properties of galaxies themselves, and the classification of their morphology plays an important role in the subsequent analysis and research.In this paper, we use the photometry image of galaxy in GalaxyZoo2, select the data set according to the threshold and perform data augmentation, and apply ResNeXt to the classification of galaxy morphology, which realizes the automatic extraction, recognition and classification of galaxy morphological features.Based on the results of ResNeXt's galaxy morphology classification, five groups of comparative experiments are carried out.The five groups of comparison experiments include comparing different versions of ResNeXt model, comparing classical convolutional neural network model, comparing the latest image classification model in the last two years, comparing the simplest convolutional neural network model, and comparing the human eye.The experimental results show that the galaxy morphology classification accuracy based on ResNeXt101 network model is the highest.

Citation Download Citation

Yang Yu "Galaxy morphology classification based on ResNeXt", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125664Z (28 March 2023); https://doi.org/10.1117/12.2667464

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available