Auroral Image Classification Based on Second-Order Convolutional Network and Channel Attention Awareness

Abstract

Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a novel method named learning representative channel attention information from second-order statistics (LRCAISS) is proposed. The LRCAISS is highlighted with two innovative techniques: a second-order convolutional network and a novel second-order channel attention block. The LRCAISS extends from Resnet50 architecture by incorporating a second-order convolutional network to capture more detailed statistical representation. Meanwhile, the novel second-order channel attention block effectively recalibrates these features. LACAISS is evaluated on two public ground-based auroral image datasets, and the experimental results demonstrate that LRCAISS achieves competitive performance compared to existing methods.

Keywords

• second-order convolutional network
• second-order channel attention
• auroral image classification
• ground-based auroral image datasets
• covariance normalization