Feature Selection Based on the Correlation of Sparse Coefficient Vectors with Application to SAR Target Recognition

This paper proposes a feature selection method for the synthetic aperture radar (SAR) target recognition problem based on multi-feature decision fusion that leverages the correlation between sparse coefficient vectors. In the proposed method, sparse representation-based classification (SRC) was applied to solve the coefficient vectors of the individual features, and their correlation was defined. Accordingly, the best combination of features was obtained from the mutual correlation matrix and calculation of the nonlinear correlation information entropy. By investigating the stable intrinsic correlation between the selected features using a joint sparse representation, the target label was determined from the reconstruction errors. Experiments were performed under the standard operating condition, configuration variance, and depression angle variance based on the MSTAR dataset. The average recognition rates of the proposed method for these scenarios reached 99.23%, 96.86%, and 97.46% (30 degrees depression angle) and 74. 64% (45 degrees depression anglc). A comparison with three existing SAR target recognition methods further validated the effectiveness and robustness of the proposed method.