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Dayside Corona Autora Detection based on Sample Selection and AdaBoost Algorithm

Gao, Lingjun, Gao, Xinbo, 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 currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:28192)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

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 processing
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: J. Harries
Date Deposited: 27 Sep 2011 11:18 UTC
Last Modified: 16 Nov 2021 10:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/28192 (The current URI for this page, for reference purposes)

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