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.

翻译：极光分类在极地研究中起着至关重要的作用。然而，当前的极光分类研究主要基于单波长（通常为557.7纳米）拍摄的图像。在其他波长下获取的图像相对被忽视，多波长信息的整合仍是一个待深入探索的领域。这一局限导致复杂极光模式的分类准确率较低。此外，无论采用传统机器学习还是深度学习方法，这些研究均未在准确性与速度之间取得令人满意的平衡。为解决这些挑战，本文提出一种基于多视角方法的轻量级极光多波长融合分类网络MLCNet。首先，我们开发了名为LCTNet的轻量级特征提取主干网络，以提高分类速率并应对日益增长的极光观测数据量。其次，考虑到极光中存在多尺度空间结构，我们设计了名为MSRM的新型多尺度重建特征模块。最后，为突出极光类别间的判别性信息，我们提出了名为LAFE的轻量级注意力特征增强模块。利用2003-2004年北极黄河站观测数据对所提方法进行验证。实验结果表明，多波长信息的融合有效提升了极光分类性能。特别地，与以往极光分类研究相比，我们的方法在分类准确率上达到最优水平，并且在精度与计算效率方面均优于现有多视角方法。