Automated Solar Cavity Detection

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?