Detection of Diffuse Auroral Events Based on Spatio-Temporal Transformer
Authors: 
Qian Wang, Shuai Wang, Wanying BaiAuthors Info & Claims
Qian Wang, Shuai Wang, Wanying BaiAuthors Info & ClaimsPages 860 - 866
Abstract
Aurora phenomenon is caused by charged particles from solar-wind colliding with atmospheric gas molecules, which is the most visible manifestation of the Sun's influence on the Earth in high-latitude area. Detection and retrieval of auroral events possessing certain space structures and temporal variations is the most important means for aurora study, but is basically done by human vision so far. Because of the great variety in morphological and motion characters, auroral event is difficult to define and represent. In this paper, we propose an improved spatio-temporal transformer network to represent auroral event based on all-sky auroral images observed years 2003-2009 at Yellow River Station (YRS). Specifically, a context encoder is introduced to spatio-temporal transformer network architecture to leverage the spatial and temporal information, with the consideration of uncertain rate of aurora change. The detected diffusion auroral events are consistent with human visual perception, where the accuracy is improved by 1.2% than previous methods and the recall rate is 92.2%. This result can be applied in the detection of large-scale auroral events and improves the efficiency of aurora study.
References
[1]
Han D S, Chen X C, Liu J J, An extensive survey of dayside diffuse aurora based on optical observations at Yellow River Station[J]. Journal of Geophysical Research, 2015, 120(9):7447-7465.
[2]
Han Desheng, Hu Zejun, Chen Xiangcai, New progress in the study of solar-side aurora based on observations from the Arctic Yellow River Station[J]. Polar Research, 2018, 030(003):235-250.
[3]
Syrjäsuo M T, Donovan E F. Diurnal auroral occurrence statistics obtained via machine vision[J]. Annales geophysicae. Copernicus GmbH, 2004, 22(4): 1103-1113.
[4]
Hu Z J, Yang H, D Huang, Synoptic distribution of dayside aurora: Multiple-wavelength all-sky observation at Yellow River Station in Ny-lesund, Svalbard[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2009, 71(8-9):794-804.
[5]
Yang X, Gao X, Tao D, Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection[J]. IEEE Trans Neural Netw Learn Syst, 2016, 27(1):32-46.
[6]
Zhang D, Wu W, Cheng H, Image-to-video person re-identification with temporally memorized similarity learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 28(10): 2622-2632.
[7]
Fang H, Hu H Q, Yang H G, A comparative study of the cosmic noise absorption Keograms generated by two different quiet day curve techniques[J]. Chinese Journal of Geophysics, 2015, 58(1):1-11.
[8]
Qian W, Liang J, Hu Z J, Spatial texture based automatic classification of dayside aurora in all-sky images[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2010, 72(5-6):498-508.
[9]
Zhong Y, Rui H, Ji Z, Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation[J]. Remote Sensing, 2018, 10(2):233.
[10]
Yang X, Wang N, Song B, BoSR: A CNN-based aurora image retrieval method[J]. Neural Networks, 2019, 116: 188-197.
[11]
Kavukcuoglu K, Sermanet P, Boureau Y L, Learning convolutional feature hierarchies for visual recognition[J]. Advances in neural information processing systems, 2010, 23.
[12]
Yan B, Peng H, Fu J, Learning spatio-temporal transformer for visual tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 10448-10457.
[13]
Tirupattur P, Duarte K, Rawat Y S, Modeling multi-label action dependencies for temporal action localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1460-1470.
[14]
Carion N, Massa F, Synnaeve G, End-to-end object detection with transformers[C]//European conference on computer vision. Springer, Cham, 2020: 213-229.
[15]
He K, Zhang X, Ren S, Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
[16]
Teed Z, Deng J. Raft: Recurrent all-pairs field transforms for optical flow[C]//European conference on computer vision. Springer, Cham, 2020: 402-419.
[17]
Yang X, Gao X, Song B, Aurora image search with contextual CNN feature[J]. Neurocomputing, 2018, 281: 67-77.
[18]
Liu S, Li B, Fan Y Y, Facial attractiveness computation by label distribution learning with deep CNN and geometric features[C]//2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017: 1344-1349.
[19]
Szegedy C, Vanhoucke V, Ioffe S, Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2818-2826.
[20]
Vaswani A, Shazeer N, Parmar N, Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
[21]
Zhou B, Khosla A, Lapedriza A, Learning deep features for discriminative localization[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2921-2929.
Index Terms
- Detection of Diffuse Auroral Events Based on Spatio-Temporal Transformer
Recommendations
Unsupervised Feature Learning for Temporal Segmentation of Auroral Image Sequences
AIPR '21: Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern RecognitionOptical observation of aurora can obtain extensive information about magnetosphere and solar-terrestrial electromagnetic activities. Temporal segmentation of auroral image sequences is the basis for detection and retrieval of auroral events and further ...
Detection of Diffuse Auroral Events Based on LSTM
AIPR '21: Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern RecognitionDiscrete and diffuse aurora are important categories of auroras optically observed on the ground. Some of the mechanisms and regularities of diffuse auroras are still unclear. In this work, we formulate the automatic diffuse aurora detection to learn a ...
Graph-enabled spatio-temporal transformer for ionospheric prediction
AbstractThe ionosphere, crucial for satellite navigation and radio communication, is highly sensitive to solar and geomagnetic activities. Traditional ionospheric prediction models often struggle to capture its complexities. This paper introduces a Graph-...
Comments
Information & Contributors
Information
Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 25Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)2
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format