Scattering-Aware Adaptive Dynamic Node Generation for SAR Class-Incremental Learning

Synthetic aperture radar (SAR) target recognition frequently encounters the emergence of novel targets. Updating and iterating existing models with newly arriving data constitutes an incremental learning task. However, training with only new data often causes catastrophic forgetting, leading to a substantial loss of performance on previously learned classes, while retraining the entire model is prohibitively costly. This necessitates the development of algorithms with strong continual learning capabilities. Moreover, compared with traditional optical scenarios, SAR incremental tasks pose even greater challenges: due to the imaging mechanism, target characteristics, and application scenarios, they are highly sensitive to azimuth variations, exhibit low inter-class variability, and demand lightweight solutions. To address these issues, this paper proposes a scattering-aware adaptive dynamic node generation (SDG) algorithm for class-incremental learning. First, the attributed scattering center model (ASCM) is integrated to extract representative prototype vectors, which enhance spatial attention and calibrate the parameters of the expanded backbone network. Second, a gradient-constrained and indicator-driven dynamic network expansion strategy, combined with parameter minimization, ensures both model compactness and efficiency. Finally, a multi-teacher knowledge distillation approach is introduced, where different backbones serve as distinct teachers to mitigate the low inter-class variability inherent in SAR images. Extensive experiments on the MSTAR, SAR-AIRcraft, and FUSAR-Ship datasets demonstrate that SDG achieves significant improvements in both recognition accuracy and parameter efficiency. This study thus provides a lightweight and effective solution for real-time SAR target recognition on resource-constrained platforms.