Articles | Volume 22, issue 4
https://doi.org/10.5194/angeo-22-1103-2004
© Author(s) 2004. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-22-1103-2004
© Author(s) 2004. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
02 Apr 2004
Diurnal auroral occurrence statistics obtained via machine vision
M. T. Syrjäsuo
Institute for Space Research, University of Calgary, Alberta, Canada
E. F. Donovan
Institute for Space Research, University of Calgary, Alberta, Canada
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Cited
47 citations as recorded by crossref.
- Spatial texture based automatic classification of dayside aurora in all-sky images Q. Wang et al. https://doi.org/10.1016/j.jastp.2010.01.011
- Statistical study of auroral omega bands N. Partamies et al. https://doi.org/10.5194/angeo-35-1069-2017
- Aurora image segmentation by combining patch and texture thresholding X. Gao et al. https://doi.org/10.1016/j.cviu.2010.11.011
- An integrated aurora image retrieval system: AuroraEye R. Fu et al. https://doi.org/10.1016/j.jvcir.2010.06.002
- Quiet, Discrete Auroral Arcs—Observations T. Karlsson et al. https://doi.org/10.1007/s11214-020-0641-7
- Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model H. Wu et al. https://doi.org/10.1029/2022SW003146
- Auroral Image Detection Based on Zernike Moments 黄. 李 https://doi.org/10.12677/CSA.2017.73027
- Extracting Auroral Key Local Structures From All‐Sky Auroral Images by Artificial Intelligence Technique Q. Yang et al. https://doi.org/10.1029/2018JA026119
- BoSR: A CNN-based aurora image retrieval method X. Yang et al. https://doi.org/10.1016/j.neunet.2019.04.012
- Aurora Image Classification with Deep Metric Learning T. Endo & M. Matsumoto https://doi.org/10.3390/s22176666
- Toolkit for incoherent scatter radar experiment design and applications to EISCAT_3D S. Hatch et al. https://doi.org/10.5194/angeo-43-633-2025
- Representation and Classification of Auroral Images Based on Convolutional Neural Networks Q. Yang & P. Zhou https://doi.org/10.1109/JSTARS.2020.2969245
- Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation P. Sado et al. https://doi.org/10.1029/2021JA029683
- Dynamic aurora sequence recognition using Volume Local Directional Pattern with local and global features B. Han et al. https://doi.org/10.1016/j.neucom.2015.07.126
- Automatic Identification of Space Hurricane Based on Transfer Learning K. XIA et al. https://doi.org/10.11728/cjss2023.02.2022-0031
- Automatic Identification of Aurora Fold Structure in All-Sky Images Q. Wang et al. https://doi.org/10.3390/universe9020079
- Automatic Classification of Auroral Images From the Oslo Auroral THEMIS (OATH) Data Set Using Machine Learning L. Clausen & H. Nickisch https://doi.org/10.1029/2018JA025274
- Complexity Heliophysics: A Lived and Living History of Systems and Complexity Science in Heliophysics R. McGranaghan https://doi.org/10.1007/s11214-024-01081-2
- The THEMIS all-sky imaging array—system design and initial results from the prototype imager E. Donovan et al. https://doi.org/10.1016/j.jastp.2005.03.027
- Auroral Image Classification With Deep Neural Networks A. Kvammen et al. https://doi.org/10.1029/2020JA027808
- Auroral Image Classification Based on Second-Order Convolutional Network and Channel Attention Awareness Y. Hu et al. https://doi.org/10.3390/rs16173178
- Magnetic local time (MLT) dependence of auroral peak emission height and morphology N. Partamies et al. https://doi.org/10.5194/angeo-40-605-2022
- Auroral breakup detection in all-sky images by unsupervised learning N. Partamies et al. https://doi.org/10.5194/angeo-42-103-2024
- Automatic Classification of Dayside Aurora in All-Sky Images Using a Multi-Level Texture Feature Representation S. Han et al. https://doi.org/10.4028/www.scientific.net/AMR.341-342.158
- A comprehensive high‐throughput analysis of substorms observed by IMAGE magnetometer network: Years 1993–2003 examined E. Tanskanen https://doi.org/10.1029/2008JA013682
- Numeric Image Features for Detection of Aurora M. Syrjasuo & N. Partamies https://doi.org/10.1109/LGRS.2011.2163616
- Automatic Aurora Image Classification Framework Based on Deep Learning for Occurrence Distribution Analysis: A Case Study of All‐Sky Image Data Sets From the Yellow River Station Y. Zhong et al. https://doi.org/10.1029/2019JA027590
- Ionosphere variability II: Advances in theory and modeling I. Tsagouri et al. https://doi.org/10.1016/j.asr.2023.07.056
- Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image C. Niu et al. https://doi.org/10.1109/TGRS.2018.2848725
- Substorm evolution of auroral structures N. Partamies et al. https://doi.org/10.1002/2015JA021217
- Morphologies of omega band auroras N. Sato et al. https://doi.org/10.1186/s40623-017-0688-1
- Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation Y. Zhong et al. https://doi.org/10.3390/rs10020233
- Midnight sector observations of auroral omega bands J. Wild et al. https://doi.org/10.1029/2010JA015874
- Automated detection of pulsating aurora using deep learning methods F. Wu et al. https://doi.org/10.3389/fspas.2026.1852793
- Omega band pulsating auroras observed onboard THEMIS spacecraft and on the ground N. Sato et al. https://doi.org/10.1002/2015JA021382
- Solar cycle and diurnal dependence of auroral structures N. Partamies et al. https://doi.org/10.1002/2013JA019631
- A comparative study of auroral morphology distribution between the Northern and Southern Hemisphere based on automatic classification Q. Yang & Z. Hu https://doi.org/10.5194/gi-7-113-2018
- Theoretical formulation and experimental validation of brightness evaluation using digital cameras N. Shimoji & M. Okizaki https://doi.org/10.1016/j.rio.2020.100050
- Aurora retrieval in all-sky images based on hash vision transformer H. Zhang et al. https://doi.org/10.1016/j.heliyon.2023.e20609
- An automated auroral detection system using deep learning: real-time operation in Tromsø, Norway S. Nanjo et al. https://doi.org/10.1038/s41598-022-11686-8
- Automated Classification of Auroral Images with Deep Neural Networks Z. Shang et al. https://doi.org/10.3390/universe9020096
- Optimizing a deep learning framework for accurate detection of the Earth’s ionospheric plasma structures from all-sky airglow images S. Chakrabarti et al. https://doi.org/10.1016/j.asr.2024.03.014
- Global auroral imaging in the ILWS era E. Donovan et al. https://doi.org/10.1016/j.asr.2006.09.028
- Unsupervised automatic classification of all-sky auroral images using deep clustering technology Q. Yang et al. https://doi.org/10.1007/s12145-021-00634-1
- Multiview Learning for Automatic Classification of Multiwavelength Auroral Images Q. Yang et al. https://doi.org/10.1109/TGRS.2024.3392942
- Observations of the Aurora by Visible All-Sky Camera at Jang Bogo Station, Antarctica G. Jee et al. https://doi.org/10.5140/JASS.2021.38.4.203
- Machine Identification of Throat Aurora X. TONG et al. https://doi.org/10.11728/cjss2021.04.654
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