DAMS-ASCNet: Coupled Scattering-Aware Representation and Class-Aware Prototype Calibration for Open-Set SAR Target Recognition

Open-set synthetic aperture radar (SAR) automatic target recognition must not only classify familiar targets accurately, but also reject target categories that are absent from training. This task is particularly challenging because aspect-dependent scattering produces substantial and often multi-modal intra-class variation, while the unknown categories available for validation cannot fully represent the open space encountered at deployment. To address these coupled difficulties, we propose the Dynamic Adaptive Multi-scale ASC Network (DAMS-ASCNet), a unified representation–distribution–decision framework for open-set SAR recognition. First, a dynamic adaptive multi-scale angle-sensitive convolution module learns sample-dependent directional and scale responses, enabling the feature extractor to preserve informative scattering variations while suppressing irrelevant disturbances. Second, multiple prototypes are introduced to model the aspect-induced submodes of each known class, providing a more faithful class representation than a single-center description. Third, open-set decisions are performed using class-conditional prototype similarities and class-conditional calibration, with rejection thresholds estimated exclusively from validation data. The revised evaluation separates the effects of representation, training objective, prototype modeling, and calibration, and reports threshold-dependent operating-point metrics separately from threshold-independent score-discrimination metrics. Experiments on the MSTAR open-set benchmark show that, on the original open-set split, the proposed framework with validation-only class-conditional calibration achieves 80.33±2.96% open-set accuracy and 81.27±5.48% unknown recall over three seeds; additional held-out class compositions reveal substantial sensitivity to the choice of unknown classes. Controlled ablations further show that the matched training objective materially affects the calibrated operating point, while the architecture-level advantage over an ordinary-convolution counterpart is modest; excessive feature compactness can also be detrimental to open-set separability.