Synthetic Aperture Radar (SAR) target classification is generally performed by feature extracting and classification decision-making.The Block-Matching and 3D filtering (BM3D) denoising algorithm is applied to SAR images to relieve noise corruption.Afterwards,the Extreme Learning Machine (ELM) is used to classify the denoised SAR images.ELM has high classification efficiency and precision.In addition, its sensitivity to noise corruption can be effectively relieved by Bi-dimensional Empirical Mode Decomposition (BEMD) denoising algorithm.Therefore,the overall classification performance can be enhanced by combining the strengths of BM3D with that of ELM.The proposed method is tested on the MSTAR dataset and the results have proved its validity and robustness.