Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses the capability to capture the multi-view of a target by acquiring images from different azimuth angles. In this research, our team utilizes a self-developed MiniSAR system to generate multi-view SAR images of real ground armored targets and recognize targets. However, the recognition of small targets in SAR images encounters two significant difficulties. First, small targets in SAR images are prone to interference from background noise. Second, SAR target deformation arises from variations in depression angles and imaging processes. To tackle these difficulties, this paper proposes a novel SAR ground deformation target recognition approach based on a joint multi-view transformer model. The method first preprocesses SAR images based on a low-frequency priori SAR image denoising method. Next, it obtains multi-view joint information through a self-attentive mechanism, inputs joint features to the transformer structure. The outputs are jointly updated by a multi-way averaging adaptive loss function to improve the recognition accuracy of deformed targets. The experimental results demonstrate the superiority of the proposed method in SAR ground deformation target recognition, outperforming other representative approaches such as information fusion of target and shadow (IFTS) and Vision Transformer (ViT). It is concluded that the proposed method has high recognition accuracies of 98.37% and 93.86 % on the moving and stationary target acquisition and recognition (Mstar) and our SAR images dataset, respectively, in the field of SAR ground deformation target recognition. We have included links to the code and data in the abstract of this paper for ease of access. The source code and sample dataset are available at .

中文翻译：

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使用多视图联合变换器方法从新的 MiniSAR 数据集中识别 SAR 形变军事目标

准确探测地面装甲武器对于军事行动中取得主动优势至关重要。一般来说，卫星或机载合成孔径雷达（SAR）系统因其重访周期和固定飞行轨迹而面临局限性，导致目标成像的单视点，从而阻碍了小型SAR地面目标的识别。相比之下，MiniSAR具有通过获取不同方位角图像来捕获目标多视图的能力。在本研究中，我们团队使用自主研发的MiniSAR系统生成真实地面装甲目标的多视SAR图像并识别目标。然而，SAR 图像中小目标的识别遇到两个重大困难。首先，SAR图像中的小目标容易受到背景噪声的干扰。其次，SAR目标变形是由俯角和成像过程的变化引起的。为了解决这些困难，本文提出了一种基于联合多视图变换模型的SAR地面形变目标识别方法。该方法首先基于低频先验SAR图像去噪方法对SAR图像进行预处理。接下来，它通过自注意力机制获取多视图联合信息，将联合特征输入到变压器结构中。通过多路平均自适应损失函数联合更新输出，以提高变形目标的识别精度。实验结果证明了该方法在SAR地面形变目标识别中的优越性，优于目标与阴影信息融合（IFTS）和视觉变换器（ViT）等其他代表性方法。结果表明，在SAR地面形变目标识别领域，该方法在运动目标获取与识别（Mstar）和SAR图像数据集上分别具有98.37%和93.86%的高识别准确率。为了便于访问，我们在本文的摘要中包含了代码和数据的链接。源代码和示例数据集可在 处获取。