Kernel linear representation: application to target recognition in synthetic aperture radar images

摘要

A method for target classification in synthetic aperture radar (SAR) images is proposed. The samples are first mapped into a high-dimensional feature space in which samples from the same class are assumed to span a linear subspace. Then, any new sample can be uniquely represented by the training samples within given constraint. The conventional methods suggest searching the sparest representations with l(1)-norm (or l(0)) minimization constraint. However, these methods are computationally expensive due to optimizing nondifferential objective function. To improve the performance while reducing the computational consumption, a simple yet effective classification scheme called kernel linear representation (KLR) is presented. Different from the previous works, KLR limits the feasible set of representations with a much weaker constraint, l(2)-norm minimization. Since, KLR can be solved in closed form there is no need to perform the l(1)-minimization, and hence the calculation burden has been lessened. Meanwhile, the classification accuracy has been improved due to the relaxation of the constraint. Extensive experiments on a real SAR dataset demonstrate that the proposed method outperforms the kernel sparse models as well as the previous works performed on SAR target recognition. (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.