The typical classification rule for kernel sparse representation-based classifier (KSRC) is the reconstruction error minimization rule. Its computational complexity mainly depends on both the dimensionality of a subspace and the number of training samples. This paper presents an alternative classification rule, called reconstruction coefficient energy maximization, for KSRC and applies it to target recognition in synthetic aperture radar (SAR) images. The computational complexity of this rule is related to only the number of training samples, which is smaller than that of the reconstruction error minimization rule. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database indicate that KSRC is very promising in SAR image target recognition., and the reconstruction coefficient energy maximization rule outperforms the reconstruction error minimization rule in KSRC.
['Li Zhang', 'Zhiwei Tao', 'Bangjun Wang']
SAR image target recognition using kernel sparse representation based on reconstruction coefficient energy maximization rule