Adaptive multiparameter spectral feature analysis for synthetic aperture radar target recognition

Abstract

A feature extraction algorithm based on spectral clustering with adaptive multiparameters is proposed for synthetic aperture radar automatic target recognition (SAR-ATR). Spectral clustering has been widely applied in computer vision for its good performance. Meanwhile, the spectral mapping step in it has the property of feature space transformation. Spectral clustering based target feature extraction for SAR-ATR is constructed according to the framework of out-of-sample extensions in weighted kernel principal component analysis. To avoid the scaling parameter selection in spectral feature analysis (SFA) and eliminate the influence of scaling parameter on feature extraction performance as well, the multiple scaling parameters are calculated adaptively by local neighborhoods. Because the local statistics of the neighborhood of each point are taken into consideration, its performance is better than using only one fixed parameter. Based on the extracted features, target recognition is performed by the support vector machine for its good generalization capability. The experimental results show that the multiparameter SFA outperforms the principal component analysis, kernel principal component analysis and SFA with the selected scaling parameter for SAR target recognition in terms of recognition accuracy.