日侧冕状极光是太阳风与地球磁层相互作用产生的典型电离层踪迹，对其正确分类对研究空间天气活动尤为重要。根据冕状极光的形态特征，提出了一种基于静态图像分类的日侧冕状极光检测算法。首先提取极光样本图像的Gabor特征，利用K 均值聚类算法进行基于有监督聚类的训练样本选择，保证训练样本的多样性和代表性。然后引入AdaBoost算法进行特征选择并构建级联分类器实现日侧冕状极光的检测。在北极黄河站采集到的实测极光图像数据库上所做的分类实验结果表明了算法的有效性。

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.