Deep Learning Applications Based on WISE Infrared Data: Classification
  of Stars, Galaxies and Quasars
Guiyu Zhao Bo Qiu A-Li Luo Xiaoyu Guo Lin Yao Kun Wang Yuanbo Liu
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 demonstrates 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 (AUC) 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, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster
inference. The above proves that IICnet is an effective method to classify
infrared sources.