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A Comparative Evaluation of Automated Solar Filament Detection

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Abstract

We present a comparative evaluation for automated filament detection in Hα solar images. By using metadata produced by the Advanced Automated Filament Detection and Characterization Code (AAFDCC) module, we adapted our trainable feature recognition (TFR) module to accurately detect regions in solar images containing filaments. We first analyze the AAFDCC module’s metadata and then transform it into labeled datasets for machine-learning classification. Visualizations of data transformations and classification results are presented and accompanied by statistical findings. Our results confirm the reliable event reporting of the AAFDCC module and establishes our TFR module’s ability to effectively detect solar filaments in Hα solar images.

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Acknowledgements

This research and development project was supported by two NASA Grant Awards: No. NNX09AB03G, funded from the NNH08ZDA001N-SDOSC solicitation, and No. NNX11AM13A, funded from the NNH11ZHA003C solicitation. We would also like to thank our internal reviewers as well as the Big Bear Solar Observatory/New Jersey Institute of Technology and the Global High Resolution Hα Network for providing and maintaining the ftp image data archive.

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Authors and Affiliations

  1. Department of Computer Science, Montana State University, 357 EPS, Bozeman, MT, 59717-3880, USA

    M. A. Schuh, J. M. Banda & R. A. Angryk

  2. Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA

    P. N. Bernasconi

  3. Department of Physics, Montana State University, 247 EPS, Bozeman, MT, 59717-3880, USA

    P. C. H. Martens

  4. Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA, 02138, USA

    P. C. H. Martens

Authors
  1. M. A. Schuh
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  2. J. M. Banda
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  3. P. N. Bernasconi
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  4. R. A. Angryk
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  5. P. C. H. Martens
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Corresponding author

Correspondence to M. A. Schuh.

Appendix

Appendix

Table 4 Complete attribute evaluation results. Note we do not try every possible combination of attributes, as discussed in the text. The first column denotes the set of attributes used for the evaluation.
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Table 5 Comparative evaluation results for all dataset variations. The three grid sizes are separated by a horizontal line, and presented in order of: 16 × 16, 32 × 32, and 64 × 64. Time is given in seconds, and the receiver operating characteristic (ROC) and precision (PRE) are averaged over both classes.
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The appendix contains the complete tabulated results for the discussed experiments. The summarized charts and insights were gathered from this data.

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Schuh, M.A., Banda, J.M., Bernasconi, P.N. et al. A Comparative Evaluation of Automated Solar Filament Detection. Sol Phys 289, 2503–2524 (2014). https://doi.org/10.1007/s11207-014-0495-9

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  • DOI: https://doi.org/10.1007/s11207-014-0495-9

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