  Auroral classification plays a crucial role in polar research. However,
current auroral classification studies are predominantly based on images taken
at a single wavelength, typically 557.7 nm. Images obtained at other
wavelengths have been comparatively overlooked, and the integration of
information from multiple wavelengths remains an underexplored area. This
limitation results in low classification rates for complex auroral patterns.
Furthermore, these studies, whether employing traditional machine learning or
deep learning approaches, have not achieved a satisfactory trade-off between
accuracy and speed. To address these challenges, this paper proposes a
lightweight auroral multi-wavelength fusion classification network, MLCNet,
based on a multi-view approach. Firstly, we develop a lightweight feature
extraction backbone, called LCTNet, to improve the classification rate and cope
with the increasing amount of auroral observation data. Secondly, considering
the existence of multi-scale spatial structures in auroras, we design a novel
multi-scale reconstructed feature module named MSRM. Finally, to highlight the
discriminative information between auroral classes, we propose a lightweight
attention feature enhancement module called LAFE. The proposed method is
validated using observational data from the Arctic Yellow River Station during
2003-2004. Experimental results demonstrate that the fusion of multi-wavelength
information effectively improves the auroral classification performance. In
particular, our approach achieves state-of-the-art classification accuracy
compared to previous auroral classification studies, and superior results in
terms of accuracy and computational efficiency compared to existing multi-view
methods.
