FUSION OF LINEAR AND NONLINEAR CLASSIFIERS FOR KERNEL DICTIONARY LEARNING: APPLICATION TO SAR TARGET RECOGNITION

In this paper, a fusion of linear and nonlinear classification errors is introduced into kernel dictionary learning and is applied for SAR target recognition. Different from linear dictionary and classifier learning, we utilize a nonlinear mapping function to map the SAR data into a higher dimensional space for nonlinear reconstruction. Inspired by neural networks, a multilayer nonlinear classification structure combined with a linear classification is introduced into the objective function such that the reconstruction error and the two classification errors are optimized simultaneously. In addition, we also use the Gaussian function to filter the noise in SAR images and perform the principal component analysis (PCA) algorithm to extract the main components of the samples. An optimization method is developed to solve the resulting problem. Experimental results performed on the MSTAR dataset demonstrate that the proposed method outperforms some representative dictionary learning and sparse representation schemes.