Unsupervised Learning of Auroral Optical Flow for Recognition of Poleward Moving Auroral Forms

Abstract

Poleward moving auroral forms (PMAFs) are one of the most common dayside auroral phenomena, which are important to study the dynamics of the polar cusp. In this article, we propose an unsupervised auroral optical flow (UnAurFlow) model for recognizing the PMAFs from auroral image sequences captured by all-sky imagers. Since auroral shape and illumination change continuously and randomly during the evolution, and auroral motion violates the brightness constancy assumption, UnAurFlow designs an auroral deformation detection module based on bidirectional optical flow and exploits the census transform to compensate for the auroral luminosity changes. UnAurFlow is trained in an unsupervised manner, which circumvents the difficulty of manually labeling auroral optical flow. The validity of UnAurFlow is qualitatively and quantitatively evaluated using auroral observations in 2003–2005 at the Arctic Yellow River Station. Based on the auroral optical flow computed by UnAurFlow, and combining with ResNet and the attention mechanism, PMAFs are automatically recognized with a precision of 84.77% from continuous auroral observations, which outperform the state-of-the-art unsupervised optical flow algorithms. The temporal occurrence distribution of PMAFs is prenoon–postnoon asymmetric under negative interplanetary magnetic field (IMF) By conditions, which is in good agreement with the previous statistical study.