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

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 B z in magnetic clouds

Filaments observed in H  on 1 January 2003 at 1708 UTC (BBSO image)

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 Kegl’s algorithm for finding the principal curve defined by a set of points

Detected filaments with borders outlined.

Filaments with spines indicated.

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

Barbs indicated by white lines.

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

Deducing filament chirality from barb counts.

The solar disk observed in H  on 30 June 2002 at 1540 UTC (BBSO image). Ten filaments identified, five filaments classified.

Contoured filament with first approximation to spine.

Second approximation.

Fourth approximation.

Sixth approximation.

Eighth approximation.

Final approximation to spine and classification of filament.

Southern hemisphere filament rests in a right-handed flux rope. Solar disk in H  on 22 August 2002 at 1603 UTC (BBSO image)

Northern hemisphere filament rests in a left-handed flux rope. Mirror image would be associated with right-handed flux rope.

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 can’t be found Estimate geoeffectiveness from filament position on the disk and magnetic indices

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

X-ray Sigmoid

Algorithm developed for filament detection can be used on sigmoids.

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 x 10 6 K, but often only 10 6 K images are available

Traditional Image Recognition Algorithms Used to identify rigid shapes Rely on edge detection Extract features: vertices, lines, circular arcs, general curves Test geometric constraints

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.

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

Characterizing a Shape with Curvature Curvature is change in tangent angle per change in arc length Counter-clockwise curving lines have positive curvature.

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

Interpreting the curvature- arclength plot Unique features: position of extrema and zeros; number of zeros; area under the curve; length of perimeter

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

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

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

Conclusions Developed algorithm for automatic detection and classification of H  filaments Developed algorithm for automatic detection of sigmoids Test results: sigmoid detector successfully flags periods of high activity