Automatic detection and classification of coronal mass ejections

Document Type

Article

Publication Date

9-1-2006

Abstract

We present an automatic algorithm to detect, characterize, and classify coronal mass ejections (CMEs) in Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The algorithm includes three steps: (1) production running difference images of LASCO C2 and C3; (2) characterization of properties of CMEs such as intensity, height, angular width of span, and speed, and (3) classification of strong, median, and weak CMEs on the basis of CME characterization. In this work, image enhancement, segmentation, and morphological methods are used to detect and characterize CME regions. In addition, Support Vector Machine (SVM) classifiers are incorporated with the CME properties to distinguish strong CMEs from other weak CMEs. The real-time CME detection and classification results are recorded in a database to be available to the public. Comparing the two available CME catalogs, SOHO/LASCO and CACTus CME catalogs, we have achieved accurate and fast detection of strong CMEs and most of weak CMEs. © Springer 2006.

Identifier

33748764648 (Scopus)

Publication Title

Solar Physics

External Full Text Location

https://doi.org/10.1007/s11207-006-0114-5

e-ISSN

1573093X

ISSN

00380938

First Page

419

Last Page

431

Issue

2

Volume

237

Grant

ATM 0548956

Fund Ref

National Science Foundation

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