Neighborhood Geometric Center Scaling Embedding for SAR ATR

Feature extraction from high-dimensional synthetic aperture radar images is one of the key steps for SAR automatic target recognition. In this paper, we propose a new approach to SAR image feature extraction that is named neighborhood geometric center scaling embedding, which is based on manifold learning theory. In our framework, neighborhood geometric center scaling is introduced to construct neighborhood relationships. The samples are endowed with clear clustering directions in dimensionality reduction, and the classification is better conducted in the feature space than in the original space. Moreover, by introducing neighborhood geometric center scaling, the influence of neighbor parameters on recognition performance is reduced effectively. The experiment based on the Moving and Stationary Target Acquisition and Recognition database shows that the proposed method has better recognition performance and higher stability than other methods.