SAR Target Configuration Recognition Using Tensor Global and Local Discriminant Embedding

This letter proposes a method that can preserve the global and local discriminative information based on the tensor representation to achieve feature extraction for synthetic aperture radar (SAR) target configuration recognition. We model SAR images of targets with different configurations as different manifolds, and each manifold is represented as a collection of maximal linear patches (MLPs), each depicted by a subspace. The manifold-to-manifold distance and subspace-to-subspace distance are used to maintain the global discriminative structure of data. Meanwhile, point-to-point distance (PPD) in an MLP is exploited to keep the local discriminative information of data. These two terms are then integrated to maintain the structure of data. Experimental results on the moving and stationary target automatic recognition (MSTAR) database demonstrate the effectiveness of the proposed method.