Solar Physics

, Volume 228, Issue 1–2, pp 119–135 | Cite as

Automatic Solar Filament Detection Using Image Processing Techniques

  • Ming Qu
  • Frank Y. Shih
  • Ju Jing
  • Haimin Wang


We present an automatic solar filament detection algorithm based on image enhancement, segmentation, pattern recognition, and mathematical morphology methods. This algorithm cannot only detect filaments, but can also identify spines, footpoints, and filament disappearances. It consists of five steps: (1) The stabilized inverse diffusion equation (SIDE) is used to enhance and sharpen filament contours. (2) A new method for automatic threshold selection is proposed to extract filaments from local background. (3) The support vector machine (SVM) is used to differentiate between sunspots and filaments. (4) Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are used to determine the filament properties. (5) Finally, we propose a filament matching method to detect filament disappearances. We have successfully applied the algorithm to Hα full-disk images obtained at Big Bear Solar Observatory (BBSO). It has the potential to become the foundation of an automatic solar filament detection system, which will enhance our capabilities of forecasting and predicting geo-effective events and space weather.


Support Vector Machine Diffusion Equation Space Weather Image Enhancement Image Processing Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Ming Qu
    • 1
  • Frank Y. Shih
    • 1
  • Ju Jing
    • 2
    • 3
  • Haimin Wang
    • 2
    • 3
  1. 1.College of Computing SciencesNew Jersey Institute of TechnologyNewarkU.S.A.
  2. 2.Center for Solar-Terrestrial ResearchNew Jersey Institute of TechnologyNewarkU.S.A.
  3. 3.Big Bear Solar ObservatoryBig Bear CityU.S.A.

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