A Practical Kernel Criterion for Feature Extraction and Recognition of MSTAR SAR Images

Complete kernel fisher discriminant analysis (CKFDA) is essentially a practical nonlinear feature extraction criterion based on kernel trick. The process is divided into two phases, i.e., kernel principal component analysis (KPCA) and linear discriminant analysis (LDA). This work uses two different kinds of CKFDA methods to extract the features of MSTAR SAR images: one only obtains the regular information in "single discriminant space", the other gains regular and irregular information in "double discriminant subspaces". The inspiring recognition results verify that the features not only overcome aspect sensitivity existent in SAR images, but also are robust to variants within the target classes which have small configuration differences