The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low...The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses.In this paper,the deep learning classification network,IICnet(Infrared Image Classification network),is designed to classify sources from WISE images to achieve a more accurate classification goal.IICnet shows good ability on the feature extraction of the WISE sources.Experiments demonstrate that the classification results of IICnet are superior to some other methods;it has obtained 96.2%accuracy for galaxies,97.9%accuracy for quasars,and 96.4%accuracy for stars,and the Area Under Curve of the IICnet classifier can reach more than 99%.In addition,the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16,GoogleNet,ResNet34,Mobile Net,EfficientNetV2,and RepVGG-fewer parameters and faster inference.The above proves that IICnet is an effective method to classify infrared sources.展开更多
基金supported by the Natural Science Foundation of Tianjin(22JCYBJC00410)the Joint Research Fund in Astronomy,National Natural Science Foundation of China(U1931134)。
文摘The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses.In this paper,the deep learning classification network,IICnet(Infrared Image Classification network),is designed to classify sources from WISE images to achieve a more accurate classification goal.IICnet shows good ability on the feature extraction of the WISE sources.Experiments demonstrate that the classification results of IICnet are superior to some other methods;it has obtained 96.2%accuracy for galaxies,97.9%accuracy for quasars,and 96.4%accuracy for stars,and the Area Under Curve of the IICnet classifier can reach more than 99%.In addition,the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16,GoogleNet,ResNet34,Mobile Net,EfficientNetV2,and RepVGG-fewer parameters and faster inference.The above proves that IICnet is an effective method to classify infrared sources.
