Automated Detection and Characterization of Solar Filaments and Sigmoids

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Automated Detection and Characterization of Solar
Filaments and Sigmoids

• K. Wagstaff, D. M. Rust,
• B. J. LaBonte and P. N. Bernasconi
• Johns Hopkins University Applied Physics
  Laboratory
• Laurel, Maryland USA
• Solar Image Recognition Workshop
• Brussels
• October 23-24, 2003

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Objectives of Solar Filament Detection and
Classification

• Report automatically on filament disappearances
• Provide warning of geomagnetic storms
• Characterize magnetic flux rope chirality and
  orientation of principal axis
• Forecast pattern of Bz in magnetic clouds

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Filaments observed in Ha on 1 January 2003 at
1708 UTC (BBSO image)
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Filament Detection Method

• Identify filament pixels
• Apply darkness threshold
• Group dark pixels into contiguous regions
• Prune out small dark regions and artifacts
• Draw contours around filament boundary
• Find spines (filament centerlines)
• Use simplified Kegls algorithm for finding the
  principal curve defined by a set of points

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Detected filaments with borders outlined.
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Filaments with spines indicated.
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Find Barbs (protrusions from filament)

• Identify points farthest from the spine
• Follow boundary in each direction to find bays,
  i.e. local minimum distances from spine
• Establish each barb centerline by connecting the
  farthest point to the midpoint of left and right
  bays

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Barbs indicated by white lines.
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Chirality (handedness) Classification

• Calculate angle between barb centerline and spine
• Classify barbs by obtuse and acute angles
• Assign filament chirality based on majority
  classification right-handed for acute angles
  left-handed for obtuse angles

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Deducing filament chirality from barb counts.
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The solar disk observed in Ha on 30 June 2002 at
1540 UTC (BBSO image). Ten filaments
identified, five filaments classified.
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Contoured filament with first approximation to
spine.
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Second approximation.
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Fourth approximation.
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Sixth approximation.
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Eighth approximation.
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Final approximation to spine and classification
of filament.
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Solar disk in Ha on 22 August 2002 at 1603 UTC
(BBSO image)
Southern hemisphere filament rests in a
right-handed flux rope.
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Northern hemisphere filament rests in a
left-handed flux rope.
Mirror image would be associated with
right-handed flux rope.
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Future Developments

• Make detection algorithm more robust
• Test against man-made lists
• Compare filament positions on successive images
  after correcting for solar rotation
• Set alarm bit if filament cant be found
• Estimate geoeffectiveness from filament position
  on the disk and magnetic indices

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Sigmoid Detection

• Sigmoid elongate structure, S or inverse-S
  shape signal of enhanced CME probability
• Present method observers watching 24 hr/day
• Improved space weather forecasts require
  automatic, accurate sigmoid detection

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X-ray Sigmoid
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Algorithm developed for filament detection can be
used on sigmoids.
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Sigmoid Detection Problems

• Structure and intensity not well correlated
• Intensity dynamic range as high as 1000
• Internal structure makes detection dependent on
  spatial resolution
• Visibility varies with temperature. Visibility
  is best at 2 - 4 x 106 K, but often only 106 K
  images are available

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Traditional Image Recognition Algorithms

• Used to identify rigid shapes
• Rely on edge detection
• Extract features vertices, lines, circular arcs,
  general curves
• Test geometric constraints

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Traditional Image Recognition

• Edge detection creates a map of edges
• Map determines key features
• Features compared to the model
• If enough features satisfy the constraints of the
  model, then the object is identified.

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Image Contouring

• Threshold image at different intensity levels
• Lines of equal intensity create closed contours
• Closed contours have distinct shapes

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Characterizing a Shape with Curvature

• Curvature is change in tangent angle per change
  in arc length
• Counter-clockwise curving lines have positive
  curvature.

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Estimating Curvature from Discreet Data

• Every curve is given by an iterated sequence of
  points
• k-curvature algorithm used for discreet data
• Computing exact curvature is impossible

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Interpreting the curvature-arclength plot

• Unique features position of extrema and zeros
  number of zeros area under the curve length
  of perimeter

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Sample Case 1 Non-Sigmoid

• Number of Regions between zeros 6
• Extrema at s 0.05, 0.18, 0.35, 0.60,
  0.70, 0.93
• Area under the curve in each region
• -2.88, 0.09, -2.89, 0.08, -2.60, 1.03

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Sample Case 2 Sigmoid

• Number of Regions between zeros 4
• Extrema at s 0.36, 0.50, 0.83, 0.94
• Area under the curve in each region
• -4.36, 0.57, -4.53, 0.61

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Successes and Problems

• 8 out of 10 Sigmoids Correctly Identified
• 6 false detections in 4 different images
• Reasons for False Detections
• Sigmoids are not yet precisely defined
• Sigmoids are often superposed on complicated
  background
• Recent Developments
• Algorithm refined and tested on SXI and EIT
  images
• Web-based implementation operates on real-time
  images

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Conclusions

• Developed algorithm for automatic detection and
  classification of Ha filaments
• Developed algorithm for automatic detection of
  sigmoids
• Test results sigmoid detector successfully
  flags periods of high activity