The Rigid Non-cooperative Target Recognition in Practice Based on the Sparse Representation

Abstract

In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its application in pattern recognition, due to its high recognition rate, robustness to corruption and occlusion, and little dependence on the features etc. However, sufficient training samples are always required by the sparse representation method for the effective recognition. In practical applications, it is generally difficult to obtain the sufficient training samples of test targets, especially non-cooperative targets. So the key issues in the target recognition based on the sparse representation are to obtain sufficient training samples in different scales, angles, and different illumination conditions, and to construct the over-complete dictionary with discriminative ability. In this paper, a new sparse representation-based framework is proposed for the rigid non-cooperative target recognition in the practical applications, in which the training samples are drawn from the simulation models of real targets. The experimental results show that the proposed solution is effective for the rigid non-cooperative target recognition in the practical application, especially, where the desired features of the sparse representation method are kept.