Automatic Detection of Corona Mass Ejections Using Pattern Recognition Techniques

Details Automatic Detection of Corona Mass Ejections Using Pattern Recognition Techniques Automatic Detection of Corona Mass Ejections Using Pattern Recognition Techniques

May 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agusmsp23a..03q&link_type=abstract

American Geophysical Union, Spring Meeting 2005, abstract #SP23A-03

Physics

7599 General Or Miscellaneous

Scientific paper

We present an automatic method to detect and categorize Corona Mass Ejections (CMEs) using the advanced pattern recognition technique. Our method is applied to LASCO (Large Angel Spectrometric Coronagraph) C2 and C3 images. CMEs are very complex features that are varied from time to time. The noises on the images also reduce the accuracy of the CME detections. Therefore, we propose different procedures for different structural CMEs that are described by Howard et al. (1985). There are three steps in our detection and characterization. (1)Preprocess: our program correlates C2 with C3 images, normalizes the images to the same contrast level and remove noises. Three original images are used to produce two difference images, and images are reformatted to angular images. (2) Characterization: automatic thresholding and morphology methods are used to segment and identify the principal objects from the images. CMEs are characterized according to their intensity, height, span, velocity and mass. (3) Classification: by detail study, twenty features are proposed to represent a CME. With these twenty features, the SVM classifiers are able to detect CMEs and categorize CMEs.

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