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Automated Solar Cavity Detection

This research presents a novel method for automating the detection of solar cavities through advanced image processing and pattern recognition techniques. Leveraging data from the Solar Dynamics Observatory, we address the challenges posed by substantial data volumes and varying event detection accuracy. By employing Haar-like features within a cascade of classifiers, we significantly improved detection rates to approximately 96%, while reducing computation time to under one second per image. Future work will focus on enhancing detection methods and reducing false positives to better predict solar events.

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Automated Solar Cavity Detection

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  1. Automated Solar Cavity Detection Image Processing & Pattern Recognition Athena Johnson

  2. Outline • Introduction • Background • Problem Statement • Proposed Solution • Experiments • Conclusions • Future Work

  3. Introduction

  4. background • Solar Dynamics Observatory (SDO) • Extreme Ultraviolet Variability Experiment (EVE) • Helioseismic and Magnetic Imager (HMI) • Atmospheric Imaging Assembly (AIA) • 1.5 Terabytes (TB) of data per day

  5. Atmospheric Imaging Assembly (AIA) • Images the Corona of the Sun • Study of solar storms • How they are created? • How they propagate upward? • How they emerge from the Sun? • How magnetic fields heat the corona?

  6. SOLAR CAVITIES • Currently an increase in implementations focused on Solar Cavities • Off limb structures • Darker elliptical structure, encompassed by lighter regions • Hypothesized to be precursors to solar events • Aid in establishing a predictive solar weather system

  7. SOLAR CAVITIES • Labrosse, Dalla and Marshall (2010) • Radial intensity profiles • Support Vector Machine (SVM) • Region growing • Calculation of metrics • Running difference on subsequent images

  8. SOLAR CAVITIES • Durak and Nasraoui (2010) • Exraction of principal contours • Calculations on contours • Adaboost

  9. Problem statement • Computation times • Detections based on metrics • Weak events missed • Multiple detections • Multiple events missed • Low hit rates

  10. Haar Classifier • Method that Paul Viola and Michael Jones published in 2001 • Four key concepts • Haar-like features • Integral Image • Adaboosting • Cascade of Classifiers

  11. Haar-Like Features • Aids in satisfying real time requirements • Rectangular regions • Reduces Computation

  12. Integral images • Rapid computation of Haar-like features

  13. Integral images Original Image Integral Image 50-17-5+2 = 30 8+6+2+5+6+3 = 30

  14. adaboosting • Aids in increasing the accuracy and speed • Begins with uniform weights over training examples • Obtain a weak classifier • Update weights Weak Classifier h1(x)

  15. adaboosting Weak Classifier h2(x) Weak Classifier h3(x)

  16. adaboosting • Weak classifiers combined to form the strong classifier

  17. Cascade of classifiers • Increases the speed of detections • All Haar-like features from all stages combined into a final Classifier Model • Cascade of boosted classifiers with Haar-like features

  18. Cascade of classifiers • A series of classifiers are applied to every subwindow of image • A positive result from the first classifier, triggers evaluation from the second classifier and so on

  19. Initial solution

  20. Results • Manually selected Training Image Sets • Positive Samples = 100 • Negative Samples = 400 • ≈ 79.6% Correct detection rate was achieved

  21. Results • Missed detections in specific quadrants • Detections on the Sun’s disk • Overlapping detections

  22. Proposed Solution

  23. Minimized training sets 10 Positive Images 10 Negative Images

  24. Mark regions of interest and rotate • Deriving images from selected images • Rotation applied to both training sets

  25. Transform regions of interest • Transformations on cavities

  26. Preprocessing • Edge Detection • Hough Lines • Calculate the radius

  27. Results • Derived Training Image Sets • Initial image in sets = 10 • Positive Samples = 3600 • Negative Samples = 3600 • ≈ 96% Correct detection rate was achieved

  28. Final image with detections

  29. Conclusion • Less manual work • Short training times • < 22 hours • Wider range of detections • Weak and strong cavities • Fast run times • < 1 second per image • Higher hit rates

  30. Future work • Technique Improvement • Reduction of False Positives • Contour Detections • Template Matching • Customized Haar-like features

  31. Future work • Find optimal number of training sets • Extract Metrics • User Interface

  32. QUESTIONS?

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