Few-Shot Multistatic ISAR Target Classification Based on Multilevel Feature Reconstruction

The collaborated observation of multi-static ISAR can provide comprehensive information for air/space target classification. However, under limited sample conditions, the existing methods is challenged by effective information fusion and distinguishable features extraction. To tackle these issues, a few-shot multi-static ISAR target classification method, dubbed multi-level feature reconstruction (MLRF), is proposed in this article, which comprises two key components: inter-station feature reconstruction (ISFR) and inter-class feature reconstruction (ICFR). Specifically, ISFR facilitates complementary information mining and redundant information suppression by multi-static feature alignment and reconstruction, while ICFR enhances inter-class separability and intra-class compactness via multi-class feature interaction and reconstruction. Additionally, a specialized hybrid loss is designed to ensure desired outputs of the related modules. Experimental results on multi-static ISAR dataset of satellite targets demonstrate that the proposed method significantly improves the few-shot classification accuracy, and ablation studies and visual analysis further validate the effectiveness of each module.