In order to improve the performance of target recognition in SAR images,this paper proposes a method combining Kernel Sparse Representation-based Classification (KSRC) and augmented dictionary based on traditional Sparse Representation-based Classification(SRC).The KSRC introduces nonlinear kernel function on the basis of SRC,so as to improve the representation ability of the classifier for nonlinear data relationships.By using original training samples,the augmented dictionary expands the original dictionary through noise addition and partial occlusion to improve its adaptability to typical Extended Operating Conditions (EOC).At the same time,with the help of KSRC,the augmented dictionary further improves the coverage of other related EOCs,thus the effectiveness of the proposed method for other EOCs can be upgraded.Experiments carried on the MSTAR dataset under Standard Operating Conditions (SOC) and EOCs including noise interference and partial occlusion shown the superior performance of the proposed method.