基于G2DPCA的SAR目标特征提取与识别

给出了基于广义二维主分最分析(G2DPCA)的合成孔径需达(SAR)图像目标特征提取方法.与主分量分析(PCA)相比,在寻求最优投影方向时,它直接基于二维图像矩阵而不是一维向量,在特征提取前不必将2维图像矩阵转换成1维向量.与二维主分量分析(2DPCA)相比,它可以同时去除图像行和列像素问的相关性.基于美国运动和静止目标获取与识别(MSTAR)计划录取的数据的实验结果表明,结合预处理.G2DPCA在大大降低了特征维数的同时,又改善了识别性能,并且正确识别率在97%以上,且对目标方位变化具有较好的鲁棒性.

SAR Target Feature Extraction and Recognition Based on Generalized 2DPCA

A feature extraction method based Generalized 2-dimensional Principal Component Analysis (G2DPCA) is present-ed for Synthetic Aperture, Radar (SAR) images. As opposed to PCA, G2DPCA directly seeks the optimal projective axes based on 2D image matrices rather than 1D image vectors, so image matrices do not need to be transformed previously into image, vectors. In contrast to 2DPCA, G2DPCA eliminates the correlations of images rows and columns simultaneously. Experimental results based the Moving and Stationary Target Acquisition and Recognition (MSTAR) data show that G2DPCA combining the SAR image pre-processing not only decreases feature dimensions sharply, but increases the correct recognition rotes, more than 97 %, and is ro-bust to the variation of target azimuth.