Synthetic aperture radar target recognition based on joint classification of selected monogenic components by nonlinear correlation information entropy

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

A novel method for SAR target recognition using monogenic components as basic features is proposed. By selecting a subset of monogenic components with the highest nonlinear correlation information entropy and classifying them using joint sparse representation, the recognition performance and overall reconstruction precision are improved. Experiments demonstrate the superior effectiveness and robustness of the proposed method over existing SAR target recognition methods under various operating conditions.