摘要
为提高极光图像的分类精度,提出了一种基于分层小波模型的极光图像分类算法.该算法分层提取全局和局部小波特征,通过主成分分析法对特征进行降维后,利用支持向量机分类器进行了极光图像的弧状和三种冕状的四分类.通过比较分类准确率和分类所用的时间,实现了分层小波模型中各个最优参数的选取,验证了利用主成分分析法进行特征降维的有效性,比较了文中算法与部分经典算法的分类效果.实验结果表明,文中算法在耗时允许范围内提升了分类准确率,极光图像的两两分类实验还给出了进一步提高分类准确率的方向.
In order to improve the accuracy of aurora images classification, an algorithm based on the wavelet hierarchical model is proposed. In the proposed algorithm, the global and local wavelet features are extracted hierarchically first, then reduced in dimensions through the principal component analysis and used to classify the arc and three corona aurora images by the use of the support vector machine. By comparing the classification accuracy and time consumption, the optimal parameters in the wavelet hierarchical model are experimentally obtained and the validity of principal component analysis in feature optimization is verified. Experimental results show that the proposed algorithm improves the classification accuracy to a great degree with an acceptable time consumption compared with classical algorithms. Classification results between each two types of aurora images also provide some potential ways to improve the accuracy.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2013年第2期18-24,共7页
Journal of Xidian University
基金
国家自然科学基金重点资助项目(41031064)
国家自然科学基金资助项目(60902082)
2010年海洋公益性行业科研专项经费资助项目(201005017)
陕西省自然科学基金资助项目(2011JQ8019)
中央高校基本科研业务费资助项目(JY10000902016)
关键词
极光图像分类
分层小波模型
主成分分析
支持向量机
aurora image classification
wavelet hierarchical model
principal component analysis （PCA）
support vector machine （SVM）
