The features employed for synthetic aperture radar (SAR) target recognition have evolved from traditional SAR target geometric features and pattern features to modern deep features, indicating a trend of increasing recognition accuracy but decreasing feature interpretability. Therefore, the fusion of multidimensional features has been investigated by many researchers. Existing feature fusion methods typically involve simple concatenation or addition of geometric features and pattern features with deep features, or directly incorporating them into deep networks. However, such fusion methods mentioned above inadequately consider the potential conflicts between features and hard to fully exploit multidimensional features. To solve the above problem, a multidimensional feature joint learning framework (MFJL-Framework) that serves the SAR target recognition task is proposed in this article, which consists of three models. Specifically, the SGC-GA-Model can select pattern features for SAR targets based on geometric feature constraints, the Global and local Feature Information interaction Capture model (GFIC-Model) can select deep features with high-level abstract semantics, and the MFFS-Model can complement and fuse these two types of features to maximize the utilization of feature information. Experiments and comprehensive ablation studies on four datasets, namely OpenSARShip-1.0, FUSAR-Ship, MSTAR-T72Variants, and SAR-AIRcraft-1.0, collectively demonstrate that the recognition performance of our proposed FJL-Framework outperforms the current state-of-the-art methods.

中文翻译：

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SAR目标识别的特征联合学习


合成孔径雷达（SAR）目标识别所采用的特征已从传统的SAR目标几何特征和模式特征发展到现代的深层特征，呈现出识别精度提高但特征可解释性降低的趋势。因此，多维特征的融合受到了许多研究者的研究。现有的特征融合方法通常涉及几何特征和模式特征与深层特征的简单串联或相加，或者直接将它们合并到深层网络中。然而，上述融合方法没有充分考虑特征之间潜在的冲突，难以充分利用多维特征。为了解决上述问题，本文提出了一种服务于SAR目标识别任务的多维特征联合学习框架（MFJL-Framework），该框架由三个模型组成。具体来说，SGC-GA-模型可以基于几何特征约束为SAR目标选择模式特征，全局和局部特征信息交互捕获模型（GFIC-模型）可以选择具有高级抽象语义的深层特征，而MFFS-模型可以对这两类特征进行补充和融合，最大限度地利用特征信息。对四个数据集（即 OpenSARShip-1.0、FUSAR-Ship、MSTAR-T72Variants 和 SAR-AIRcraft-1.0）的实验和综合消融研究共同证明，我们提出的 FJL 框架的识别性能优于当前的最佳水平。艺术方法。