Azimuth-Aware Subspace Classifier for Few-Shot Class-Incremental SAR ATR

With the rapid acquisition of high-resolution synthetic aperture radar (SAR) images, new categories are continually observed with few-shot instances in openly noncooperative scenarios. Powering an SAR automatic target recognition (SAR ATR) system with an ability of few-shot class-incremental learning (FSCIL) is nontrivial. Observing the pronounced azimuth-dependence and part-sparsity of targets in SAR images, an azimuth-aware subspace classifier (AASC) on the Grassmannian manifold is proposed to tackle the FSCIL of SAR ATR stably and accurately. In the AASC, losses covering both semantic and manifold facets, which include semantic margin separation (SMS), deep subspace separation (DSS), and structure less forgetting (SLF), are designed to strike both the intrinsic model's stability and plasticity dilemma and domain-specific challenges. For plasticity, the novel-to-old semantic margins are enlarged by the SMS loss for knowledge transferring while avoiding inappropriate adaptions. The DSS loss derived from the Grassmannian geometry aims to regularize class subspaces orthogonality. For stability, semantic drifts of target spatial and global structures are punished by the SLF loss. As the periodicity and volatility of target azimuth-aware patterns, an azimuth-aware exemplar selection (AES) strategy is designed to select representative and complementary exemplars. In experiments, the advantages of the subspace classifier and the designed losses and strategies are deeply verified. Comprehensive experiments on three FSCIL scenarios derived from both airborne and spaceborne datasets, including the moving and stationary target acquisition and recognition (MSTAR), the SAR-AIRcraft-1.0, and self-collected datasets, show that our method significantly outperforms various task-specific benchmarks, verifying its effectiveness for the FSCIL in real-SAR ATR scenarios.