As for target recognition in Synthetic Aperture Radar (SAR) images,this paper employs the original images and the target reconstruction results based on attribute scattering centers for decision fusion.The Kernel Sparse Representation-based Classification (KSRC) is taken as the basic classifier to classify the original and reconstructed SAR images.The KSRC improves the classification adaptivity by introducing the kernel function,and target reconstruction can effectively reduce noises in the original SAR images.According to the energy relationship between the reconstruction results and the residual in the target reconstruction process,the noise level of the original SAR images is evaluated.Accordingly,the weights of the original images and the reconstructed images are determined.The weighted fusion method is used to process them and judge the target categories of the test samples.The proposed method is tested based on MSTAR dataset,and the experimental results prove its effectiveness.