Sparsity is a remarkable character of Synthetic Aperture Radar (SAR) image and its dimension of storage is high

so the recognition of SAR image is very difficult.In order to solve the problem

an algorithm of SAR image recognition based on sparse manifold learning is proposed.Firstly the image was enhanced in order to preserve the edge information of the objective;The second step was determining the least number of points which can get the integrate low-dimensional manifold by the spectrum of the sample covariance matrix;then utilized kernel extending of Laplacian Embedding(LE) to get the low-dimensional coordinates of the out-of-sample

at last SAR images were recognized by Kernel-based Nonlinear Representor (KNR).Experimental results on MSTAR show its feasibility and superiority by comparing with other methods.