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

Jifang Pei,Xian Liu,Jianyu Yang,Yulin Huang

doi:10.1117/1.oe.52.3.036201

Abstract

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.

Highlights

Feature extraction is one of the key steps of synthetic aperture radar automatic target recognition (SAR ATR), which can reduce the dimensions of SAR images and extract the effective discriminating feature.Generally, feature extraction methods are placed into two categories: linear and nonlinear
With the development of the support vector machine,[4] nonlinear feature extraction methods based on kernel tricks, such as kernel principal component analysis[5] (KPCA) and kernel linear discriminant analysis (KLDA),[6] have been widely applied in SAR ATR
For the purpose of seeking a lowdimensional manifold embedded in the high-dimensional data space, various manifold learning algorithms have been proposed, such as isometric feature mapping,[8] locally linear embedding[9] (LLE), Laplacian eigenmaps (LE),[10] locality preserving projections (LPP),[11] neighborhood preserving embedding (NPE),[12] and orthogonal neighborhood preserving projections (ONPP).[13]

Summary

Introduction

Feature extraction methods are placed into two categories: linear and nonlinear Classical linear methods, such as principal component analysis (PCA),[1] and linear discriminant analysis (LDA),[2] are based on the global linear structure of data. With the development of the support vector machine,[4] nonlinear feature extraction methods based on kernel tricks, such as kernel principal component analysis[5] (KPCA) and kernel linear discriminant analysis (KLDA),[6] have been widely applied in SAR ATR. A main shortcoming of the kernel tricks is that the recognition performance depends on the selection of kernel settings Another novel nonlinear method, manifold learning,[7] has been proposed on the premise that high-dimensional images lie on or near a low-dimensional manifold embedded in the high-dimensional space. For the purpose of seeking a lowdimensional manifold embedded in the high-dimensional data space, various manifold learning algorithms have been proposed, such as isometric feature mapping,[8] locally linear embedding[9] (LLE), Laplacian eigenmaps (LE),[10] locality preserving projections (LPP),[11] neighborhood preserving embedding (NPE),[12] and orthogonal neighborhood preserving projections (ONPP).[13]

Methods

Results

Conclusion