Automatic Target Recognition based on SAR images and Two-Stage 2DPCA features

摘要：

2-dimensional Principal Component Analysis (2DPCA) has received more and more attentions in recent years, since it can evaluate the covariance matrix more accurate than PCA in extracting features from 2- dimensional images. However, a drawback of 2DPCA is that it needs more features than PCA because 2DPCA only eliminates the correlations between rows. In this paper, two-stage 2DPCA is proposed to extract features from synthetic aperture radar (SAR) images to further compress the dimension of features and decrease the recognition computation. Experimental results based on MSTAR data indicate that two-stage 2DPCA can decrease feature dimensions significantly, and the target recognition performance can be improved at the same time.

作者: Liping Hu Jin Liu Hongwei Liu Bo Chen Shunjun Wu

作者单位: National Lab.of Radar Signal Processing,Xidian University,Xian,China,710071

会议类型: 国际会议

会议名称: 首届亚太合成孔径雷达会议(1st Asian and Pacific Conference on Synthetic Aperture Radar Proceedings)

会议地点: 安徽黄山

会议语种:英文

在线出版日期: 2007-11-05（万方平台首次上网日期，不代表论文的发表时间）