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.

Unknown image deformation and few-shot issues have posed significant challenges to inverse synthetic aperture radar (ISAR) target classification. To achieve robust feature representation and precise correlation modeling, this article proposes a novel two-stage few-shot ISAR classification network, dubbed as robust embedding and manifold inference (REMI). In the robust embedding stage, a multihead spatial transformation network (MH-STN) is designed to adjust unknown image deformations from multiple perspectives. Then, the grouped embedding network (GEN) integrates and compresses diverse information by grouped feature extraction, intermediate feature fusion, and global feature embedding. In the manifold inference stage, a masked Gaussian graph attention network (MG-GAT) is devised to capture the irregular manifold of samples in the embedding space. In particular, the node features are described by Gaussian distributions, with interactions guided by the masked attention mechanism. Experimental results on two ISAR datasets demonstrate that REMI significantly improves the performance of few-shot classification and exhibits robustness in various scenarios.

Restricted by observation conditions, some scarce targets in the synthetic aperture radar (SAR) image only have a few samples, making effective classification a challenging task. Although few-shot SAR target classification methods originated from meta-learning have made great breakthroughs recently, they only focus on object-level (global) feature extraction while ignoring part-level (local) features, resulting in degraded performance in fine-grained classification. To tackle this issue, a novel few-shot fine-grained classification framework, dubbed as HENC, is proposed in this article. In HENC, the hierarchical embedding network (HEN) is designed for the extraction of multi-scale features from both object-level and part-level. In addition, scale-channels are constructed to realize joint inference of multi-scale features. Moreover, it is observed that the existing meta-learning-based method only implicitly utilize the information of multiple base categories to construct the feature space of novel categories, resulting in scattered feature distribution and large deviation during novel center estimation. In view of this, the center calibration algorithm is proposed to explore the center information of base categories and explicitly calibrate the novel centers by dragging them closer to the real ones. Experimental results on two open benchmark datasets demonstrate that the HENC significantly improves the classification accuracy for SAR targets.

Restricted by the observation condition, synthetic aperture radar (SAR) automatic target classification based on deep learning usually suffers from insufficient training samples. To tackle this problem, a novel few-shot learning (FSL) framework for SAR target classification, i.e., the mixed loss graph attention network (MGA-Net), is proposed. The classification procedure of the MGA-Net consists of three main stages. In the first stage, the task set is expanded by the data augmentation module to increase diversity. In the second stage, the embedding network is designed to map samples to the embedding space with strong intra-class similarity and inter-class divergence. In the third stage, the multilayer graph attention network (GAT) is constructed and updated according to a novel mixed loss to obtain the classification result. In particular, the data augmentation module alleviates the desire of training samples under large model capacity and enhances the robustness to noise and viewing angle variation; the multilayer GAT accurately captures relations between samples by the attention mechanism; and the mixed loss increases the inter-class separability and accelerates convergence. Experimental results under various few-shot observation settings of the MSTAR and the OpenSARShip benchmark datasets demonstrate that the MGA-Net obtains higher accuracy than typical FSL methods and exhibits robustness to large depression angle variation.