Neighborhood virtual points discriminant embedding for synthetic aperture radar automatic target recognition

We propose a new feature extraction method for synthetic aperture radar automatic target recognition based on manifold learning theory. By introducing the virtual point in every sample's neighborhood, we establish the spatial relationships of the neighborhoods. When the samples are embedded into the feature space, each sample moves toward its neighborhood virtual point, whereas the virtual points with the same class label get together, and the virtual points from different classes separate from each other. This can improve the classification and recognition performance effectively. Experiments based on the moving and stationary target acquisition and recognition database are conducted to verify the effectiveness of our method. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.OE.52.3.036201]