西安电子科技大学学报（自然科学版）2017，Vol.44Issue(1)：83-88,6.DOI:10.3969/j.issn.1001-2400.2017.01.015
改进的主成分分析网络极光图像分类方法
Improved PCANet for aurora images classification
摘要
Abstract
The mysterious aurora is changeable , and the different forms of the aurora represent various physical processes which often affect our lives . So , it is of significant scientific value to classify the aurora images for the study of space physics . Based on the PCANet , a simple deep learning model , we develop an improved PCANet algorithm for aurora images classification . Firstly , the map of aurora images are extracted by the improved PCANet . Then the support vector machine is used to classify the feature of aurora images . Experimental results with the dataset obtained from the All‐sky Imager at the Chinese Arctic Yellow River Station demonstrate that the scheme can obtain higher accuracy in aurora image classification than the PCANet .关键词
极光图像/深度学习/主成分分析/二维主成分分析/主成分分析网络Key words
dayside aurora/deep learning/principle component analysis/2DPCA/PCANet分类
信息技术与安全科学引用本文复制引用
韩冰,贾中华,高新波..改进的主成分分析网络极光图像分类方法[J].西安电子科技大学学报（自然科学版）,2017,44(1):83-88,6.基金项目
国家自然科学基金资助项目（41031064，61572384）；陕西省自然科学基础研究计划资助项目（2011JQ8019）；海洋公益性行业科研专项资助项目（201005017）；教育部留学回国人员科研启动基金支持以及中央高校基本科研业务基金资助项目（K5051302008）；北京师范大学遥感科学国家重点实验室资助项目 （）
