In this paper, a grayscale wavelet moment and entropy combined feature, which can well represent the images with much
lower dimensions, is proposed for the MSTAR targets' classification, also a discriminative feature selection and
evaluation method for multi-class targets is presented. By introducing SVM as the classifier to some simulation tests, it
can be shown that the grayscale wavelet moment and entropy combined feature has good capability for translation,
scaling and rotation transformation in both local information and global information conditions, by the reasons of
wavelet moments' multi-resolution analysis, moment invariant quality and entropy's statistic quality for image disorder.
The test also confirms that this feature has a significant improvement on classificatory accuracy using SVM with a lower
feature dimension than the other features such as the single wavelet moment, Hu's moment and PCA.