A Novel SAR Target Recognition Approach under Imbalanced Categories: Constraint and Optimization

Target recognition is one of the most significant tasks in synthetic aperture radar (SAR) image interpretation. However, due to the varying difficulty in acquiring SAR images for different categories, SAR target recognition often encounters the issue of categories imbalance. This make majority categories contribute more to the loss than minority categories, yielding a decline in classification performance. To this end, a novel SAR target recognition approach under imbalanced categories is proposed. Firstly, focal loss (FL) is introduced to balance contributions of minority and majority categories to model optimization. Then, a first-order flatness constrained FL is devised to minimize the high generalization error effectively. Finally, a gradient norm aware minimization (GAM) algorithm is implemented to integrate first-order flatness into optimization process, yielding favorable recognition results for both minority and majority categories. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of our proposed method.