摘要： Extracting valuable and discriminative features is one of the crucial issues for target recognition in synthetic aperture radar (SAR) images. In this study, a feature extraction method based on robust locality discriminant projection (RLDP) is presented for SAR target recognition. To characterise the local structural information of SAR images, the manifold learning technique called the supervised locality preserving projection is introduced to learn a linear projection, with which the SAR image can be cast into an implicit feature space. Then, the authors extend t-distributed stochastic neighbour embedding to a parametric framework for optimising the linear projection. In the resulting feature space, the intrinsic neighbour relation with a certain class can be preserved. In addition, the separation between different classes can be enhanced. Unlike most local manifold learning methods, the proposed method is robust to changes of the neighbour parameter. To further analyse the non-linear structure, a useful variant of RLDP named kernel RLDP (KRLDP) is proposed. KRLDP exploits RLDP in an implicit reproducing kernel Hilbert space, where the kernel-based non-linear projection is learned to capture the non-linear structural information. Extensive experiments on moving and stationary target automatic recognition databases demonstrate the effectiveness of the proposed methods.