Dayside Corona Autora Detection based on Sample Selection and AdaBoost Algorithm

Gao, Lingjun and Gao, Xinbo and Liang, Jimin (2010) Dayside Corona Autora Detection based on Sample Selection and AdaBoost Algorithm. Journal of Image and Graphics, 15 (1). pp. 116-121. ISSN 1006-8961. (The full text of this publication is not available from this repository)

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Abstract

Dayside corona aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, and the detection of corona aurora is significant to the study of space weather activity. According to the appearance feature of corona aurora, an algorithm based on static image classification is proposed to detect dayside corona aurora. At first, Gabor features are extracted from original aurora images. Then, supervised K-means clustering is proposed to select training samples for the sake of their diversity and representative. AdaBoost algorithm is used to select features and build cascade classifiers to implement the detection of dayside corona aurora. The experimental results on the real aurora image database from Chinese Arctic YellowRiver Station illustrate the effectiveness of the proposed algorithm.

Item Type: Article
Uncontrolled keywords: Dayside corona aurora, Gabor features, AdaBoost algorithm, K-means clustering
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: J. Harries
Date Deposited: 27 Sep 2011 11:18
Last Modified: 23 May 2014 07:33
Resource URI: http://kar.kent.ac.uk/id/eprint/28192 (The current URI for this page, for reference purposes)
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