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Galaxy morphology classification with deep convolutional neural networks
Zhu, Xiao-Pan; Dai, Jia-Ming; Bian, Chun-Jiang; Chen, Yu; Chen, Shi; Hu, Chen
Department运控部/科学卫星综合运控中心
Source PublicationASTROPHYSICS AND SPACE SCIENCE
2019
Volume364Issue:4Pages:55
DOI10.1007/s10509-019-3540-1
ISSN0004-640X
Language英语
KeywordGalaxy morphology classification Deep learning Convolutional neural networks
AbstractWe propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), is applied to a sample of 28790 galaxy images from the Galaxy Zoo 2 dataset, to classify galaxies into five classes, i.e., completely round smooth, in-between smooth (between completely round and cigar-shaped), cigar-shaped smooth, edge-on and spiral. Various metrics, such as accuracy, precision, recall, F1 value and AUC, show that the proposed network achieves state-of-the-art classification performance among other networks, namely, Dieleman, AlexNet, VGG, Inception and ResNets. The overall classification accuracy of our network on the testing set is 95.2083% and the accuracy of each type is given as follows: completely round, 96.6785%; in-between, 94.4238%; cigar-shaped, 58.6207%; edge-on, 94.3590% and spiral, 97.6953%. Our model algorithm can be applied to large-scale galaxy classification in forthcoming surveys, such as the Large Synoptic Survey Telescope (LSST) survey.
Indexed BySCI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6978
Collection运控部/科学卫星综合运控中心
Recommended Citation
GB/T 7714
Zhu, Xiao-Pan,Dai, Jia-Ming,Bian, Chun-Jiang,et al. Galaxy morphology classification with deep convolutional neural networks[J]. ASTROPHYSICS AND SPACE SCIENCE,2019,364(4):55.
APA Zhu, Xiao-Pan,Dai, Jia-Ming,Bian, Chun-Jiang,Chen, Yu,Chen, Shi,&Hu, Chen.(2019).Galaxy morphology classification with deep convolutional neural networks.ASTROPHYSICS AND SPACE SCIENCE,364(4),55.
MLA Zhu, Xiao-Pan,et al."Galaxy morphology classification with deep convolutional neural networks".ASTROPHYSICS AND SPACE SCIENCE 364.4(2019):55.
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