Physical Attributes Embedded Prototypical Network for Incremental SAR Automatic Target Recognition

In Synthetic Aperture Radar (SAR) applications, the continuous emergence of new target classes poses a significant challenge to Automatic Target Recognition (ATR) systems. Adapting to the distribution of new data can induce drastic alterations in the feature space of deep models, resulting in a decline in their ability to recognize old data, termed catastrophic forgetting. To address this challenge, we propose a novel class-incremental SAR ATR method based on Physical Attributes Embedded Prototypical Network (PAEPN). PAEPN embeds physical attributes derived from electromagnetic scattering and geometric priors into the deep model to achieve stable representations. These physical attributes, determined by the target's shape, structure, and material composition, remain invariant throughout the incremental learning process, thereby enhancing the stability and interpretability of deep models. Specifically, PAEPN first extracts and integrates physical attribute priors to establish feature anchors, guiding the deep model in extracting physically consistent features and preventing drastic changes in the feature space. Second, a spatial attention enhancement strategy is introduced to enable the deep model to reliably focus on the key regions of SAR targets. Finally, feature relations that represent semantic similarity are distilled to further mitigate catastrophic forgetting. During testing, PAEPN employs the cosine distance between the sample feature and class prototypes for recognition. Comprehensive experiments on three datasets demonstrate that PAEPN outperforms existing state-of-the-art methods.