Synthetic Aperture Radar Target Recognition Using Weighted Multi-Task Kernel Sparse Representation

As an extension of traditional sparse representation (SR), kernel SR has received great interest recently in the areas of computer vision and pattern recognition. It shows a considerable capacity to map linearly inseparable data into high-dimensional feature space via nonlinear mapping technique, and has been widely used in target recognition problems. In this paper, we propose a new weighted multi-task kernel sparse representation method to solve the synthetic aperture radar (SAR) target recognition problem. To capture the spatial and spectral information of a SAR target simultaneously, the proposed method explores the monogenic signal transformation to generate multi-scale monogenic features at first. Then, the proposed method provides a unified framework, named multi-task kernel sparse representation, for SAR target classification. The framework implicitly maps monogenic features into a high-dimensional kernel feature space by using the nonlinear mapping associated with a kernel function. In the kernelized subspace, SAR target recognition is formulated as a joint covariate selection problem across a group of related tasks. Furthermore, a multi-task weight optimization scheme is developed to compensate for the heterogeneity of the multi-scale features and enhance the recognition performance. Extensive experimental results tested on the public moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that our proposed method achieves better recognition performance than other existing competitive algorithms.