Solar Physics

, Volume 289, Issue 7, pp 2503–2524 | Cite as

A Comparative Evaluation of Automated Solar Filament Detection

  • M. A. Schuh
  • J. M. Banda
  • P. N. Bernasconi
  • R. A. Angryk
  • P. C. H. Martens
Article

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.

Keywords

Automated feature finding Filaments Quantitative comparative evaluation 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • M. A. Schuh
    • 1
  • J. M. Banda
    • 1
  • P. N. Bernasconi
    • 2
  • R. A. Angryk
    • 1
  • P. C. H. Martens
    • 3
    • 4
  1. 1.Department of Computer ScienceMontana State UniversityBozemanUSA
  2. 2.Applied Physics LaboratoryJohns Hopkins UniversityLaurelUSA
  3. 3.Department of PhysicsMontana State UniversityBozemanUSA
  4. 4.Harvard-Smithsonian Center for AstrophysicsCambridgeUSA

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