中文摘要：

给出了基于广义二维主分量分析（G2DPCA）的合成孔径雷达（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间的相关性。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。

英文摘要：

A feature extraction method based Generalized 2-dimensional Principal Component Analysis （G2DPCA） is presented 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 tmnsfomaed previously into image vectors. In contrast to 2DPCA, G2DPCA eliminates the correlations of images rows and cohnnns simultaneously. Experimental results based the Moving and Stationary Target Acquisition and Recognition （MSTAR） data show that G2DPCA combining the SAR image preprocessing not only decreases feature dimensions sharply, but increases the correct recognition rates, more than 97%, and is robust to the variation of target azimuth.