Japan Geoscience Union Meeting 2019

Presentation information

[E] Poster

P (Space and Planetary Sciences ) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM11] Dynamics of Magnetosphere and Ionosphere

Wed. May 29, 2019 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Aoi Nakamizo(Applied Electromagnetic Research Institute, National Institute of Information and Communications Technology), Mitsunori Ozaki(Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University), Akiko Fujimoto(Kyushu Institute of Technology), Tomoaki Hori(Institute for Space-Earth Environmental Research, Nagoya University)

[PEM11-P26] Automatic Classification of Auroral images using optical flow feature

*Naoto Kashiwagi1, Akiko Fujimoto1, Terumasa Tokunaga1, Yoshizumi Miyoshi2, Yasunobu Ogawa3, Keisuke Hosokawa4 (1.Kyushu Institute of Technology, 2.Nagoya University, 3.National Institute of Polar Research, 4.The University of Electro-Communications )

Keywords:Aurora, Optical flow, Convolution Neural Network

The machine learning technique including the deep learning has been developed and the practical uses have increased on various field. On the study of auroral detection, the concern over the automatic image classification has risen using the approach of machine learning technique for the large statistical analysis of auroral images. The automatic auroral image classification has tried to use some techniques from computer vision, machine vision and pattern recognition. There are roughly two kinds of the selected feature: the color of image and the motion of aurora image sequences. The works based on the color feature of aurora image have been performed by Rao et al. (2014) and Tanaka et al. (2015). Rao et al. (2014) performed Support Vector Machines (SVM) classification with several local feature extraction techniques, and found that Scale Invariant Feature Transform (SIFT), OpponentSIFT, is the most effective feature for the automated classification of color aurora images. Tanaka et al. (2015) used the aurora area using the HSV model. They mentioned that it is difficult to classify the images including the diffuse aurora and cloud area. The color feature work well for the color auroral image, while the gray-scale (optically filtered) auroral images need other features on the automated classification. Blixt et al. (2006) applied the optical flow estimation to auroral image sequences. Their case study showed the useful tool for representing the motion of the aurora. In this study, we validate the optical flow estimation to extract the feature of auroral image, and automatically classify the large gray-scale auroral images using the machine learning with Convolution Neural Network (CNN). We use 63,486 gray-scale aurora images in total and use five categories for the preparation of supervised learning data, 1) aurora, 2) aurora and cloudy, 3) aurora and moon, 4) aurora, cloudy and moon, 5) noaurora. We perform the Hold-out and the Cross Validation to evaluate our classification performance. We obtained 98.58% of the classification accuracy based on Hold-out and 98.47% of the classification accuracy based on Cross Validation.