SAR Target Recognition via Joint Manifold Regularized Low-Rank Matrix Approximation

In this paper, synthetic aperture radar (SAR) image target recognition via joint manifold regularized low-rank matrix approximation (JMLMA) is presented. To capture the low-dimensional representation of SAR images, the low-rank matrix approxi mation framework is employed. However, in the actual application, targets are classified in the presence of variation in configuration and articulation, thus the underling manifold structure information may be missing in the learning process. To solve the problem, a joint manifold regularization term formed with different manifold models is proposed and incorporated into the low-rank matrix approximation framework. Hence, the pro posed method can not only obtain the low-dimension representation of SAR images, but also capture the intrinsic manifold structure in samples. We conduct experiments on pub licly available MSTAR database to verify the the effectiveness of the proposed method.