SAR Target Recognition via Joint Sparse Representation of Monogenic Components with 2D Canonical Correlation Analysis

Abstract

A synthetic aperture radar (SAR) target recognition approach is developed in this paper by exploiting the multiscale monogenic components, which are extracted from SAR images based on the 2D monogenic signal. The 2D canonical correlation analysis is then employed to analyze the correlations of the same monogenic components at different scales. Afterwards, the three monogenic components, i.e., local amplitude, local phase, and local orientation, at different scales are fused as three feature matrices, respectively. In order to further capture the correlations between different types of monogenic components, the joint sparse representation is used for target classification. Therefore, both the correlations of the same monogenic components at multiple scales and the relatedness among different types of monogenic components can be exploited in the proposed scheme. The real measured SAR images from the moving and stationary target acquisition and recognition dataset are classified to examine the validity of the proposal. Compared with some state-of-the-art SAR target recognition methods, the proposed approach is validated to be superior under both standard operating condition and several usual extended operating conditions according to the experimental results. In comparison with some other methods, which also use monogenic components as the basic features, the superiority of the proposed method demonstrates that it could better make use of the monogenic components to improve the classification performance.