Research on Mixed PCA/ICA for SAR Image Feature Extraction

摘要：

The differences between Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for feature extraction are analyzed theoretically and experimentally,and a mixed PCA/ICA transform is developed for Synthetic Aperture Radar image feature extraction.This method combines the subspace produced by PCA and the subspace generated by ICA to form a mixed subspace to be used to extract features.The mixed components features retain the information characterized by statistics of second and high orders simultaneously.Finally,combined with Support Vector Machine (SVM),the method is employed to recognition of objects in MSTAR SAR dataset.Experimental results indicate the method can improve the recognition performance slightly compared to PCA and ICA.

作者: Xiaoguang Lu Ping Han Renbiao Wu

作者单位: School of Electronic Information Engineering,Tianjin University,Tianjin 300072,China;Tianjin key lab Tianjin key lab for advanced signal processing,CAUC,Tianjin 300300,China

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）