Performance Comparison of Target Classification in SAR Images Based on PCA and 2D-PCA Features

摘要：

Feature extraction is an important step for target classification in SAR images.Principal component analysis (PCA) is common in pattern recognition,and has been used widely for target classification in SAR images.In order to utilize PCA,two-dimensional image has to be arranged to an observation vector.However,two-dimensional PCA (2D-PCA),which is developed from PCA,can extract features from twodimensional SAR image directly. Although 2D-PCA is consistent with PCA in theory essentially,which represents original data by extracting principal components with high variance values by linear transformation,they perform distinctly due to the difference of data processing methods.Based on the theoretical analysis and classification experiment using MSTAR data,this paper compares PCA and 2D-PCA systematically and roundly.

关键词： Synthetic Aperture Radar image Feature Eztraction Principal Component Analysis Two-dimensional PCA

作者: Changzhen QIU Hao REN Huanxin ZOU Shilin ZHOU

作者单位: School of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China

会议类型: 国际会议

会议名称: 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar(第二届亚太合成孔径雷达会议)

会议地点: 西安

会议语种:英文

页码: 868-871

在线出版日期: 2009-10-26（万方平台首次上网日期，不代表论文的发表时间）