LAUSR

ABSTRACT This paper proposes a new method for feature extraction of synthetic aperture radar (SAR) image based on parametric supervised t-stochastic neighbor embedding (PS-tSNE). To mitigate the rejection of dissimilar targets from the same class, aspect angles of targets are modeled to supervise and construct the distributional properties of the training data in the original space. Then, an explicit nonlinear mapping using kernel trick is proposed by an extension of non-parametric t-SNE supervised by the information of aspect angles. This method preserves the local structure of the targets of SAR images as well as possible and enables explicit out-of-sample extensions. Experimental results based on moving and stationary target automatic recognition (MSTAR) dataset illustrate the effective performance of the proposed method on visualization and recognition.