Automated Sunspot Classification and Tracking Using SDO/HMI Imagery

Abstract

Verification of an automated sunspot detection and classification algorithm is conducted utilizing two years of solar imagery from NASA's Solar Dynamics Observatory (SDO) satellite. Automated McIntosh classifications are compared against sunspot reports from the National Oceanic and Atmospheric Administrations (NOAA) Space Weather Prediction Center (SWPC) using a three-tiered comparison metric. Statistical confidence is demonstrated for algorithm performance and consistency when compared against the SWPC data set, suggesting future applications of the algorithm will perform similarly. A sunspot tracking algorithm is added to the existing code and demonstrates reliable feature tracking for time periods out to four days between consecutive images. Finally, an empirical Mount Wilson Magnetic Classification algorithm is generated with early testing exhibiting a direct match of 79.78 with SWPC magnetic classifications.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 24, 2016
Accession Number
AD1053930

Entities

People

  • Maclane A Townsend

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Change Detection
  • Coordinate Systems
  • Department Of Defense
  • Digital Images
  • Governments
  • Image Processing
  • Light Sources
  • Line Of Sight
  • Magnetic Separation
  • Regression Analysis
  • Solar Activity
  • Solar Physics
  • Space Weather
  • Sun
  • United States Government
  • Visible Spectra

Readers

  • Atmospheric Remote Sensing.
  • Computer Vision.
  • Solar Physics

Technology Areas

  • Space
  • Space - Space Objects