## 1 Introduction  
As an active microwave remote sensing detection platform, a synthetic aperture radar (SAR) has the advantages of all-day, all-weather imaging capabilities and certain penetration capabilities; hence, it is widely used in military and civilian fields [1]. Unlike optical images that can be interpreted directly by human eyes, SAR images contain rich target information. However, the task of SAR image interpretation becomes challenging due to SAR's unique imaging mechanism [2]. In addition, it is difficult to acquire and label SAR images, which requires a lot of human and material resources. Therefore, SAR automatic target recognition (SAR ATR) has become a research focus in recent years. Due to the presence of speckle noise, SAR images have specific fine-grained characteristics, which are manifested as large and small inter-class differences. As a result, the task of classifying SAR images remains challenging. With the development of deep learning theory, deep neural networks have been widely applied to various fields [3]. In the field of SAR ATR, compared with the methods based on template matching [4] and model matching [5], the methods based on deep neural networks have the advantage of automatic feature extraction. Among them, the most representative method is based on convolutional neural networks (CNNs). CNNs collect local features in a hierarchical way to obtain powerful image representation and can fully extract local texture details of SAR images. In 2022, [PERSON] et al. [6] proposed a pure convolutional neural network named ConvNeXt, which has become the most representative CNN, with a more concise algorithm and fewer parameters compared with the traditional CNNs. However, limited by the size of the convolution kernel, CNN's ability to capture global features of images is weak. As a result, it is difficult to obtain the long-distance relationship between SAR image elements, leading to the semantic information relationship between fine-grained image parts being ignored [7]. With the emergence of transformers [8], the monopoly of CNNs in the field of computer vision is gradually being broken. Unlike the architecture of CNNs, a self-attention mechanism is used by the transformer architecture for global relationship modeling. In 2020, [PERSON] et al. [9] proposed the vision transformer (ViT) model for image classification. The ViT model is the first pure transformer model applied in the field of computer vision, which solves the input problem of transformers in the image field by dividing the image into multiple patches and coding them into sequence vectors. This approach has achieved excellent performance in the field of image classification. However, the ViT model has a large number of parameters, high computational cost, and certain restrictions on the size of the input image. Aiming to address the shortcomings of ViT, [PERSON] et al. [10] proposed a Swin Transformer model with a sliding window operation and hierarchical design, which has higher efficiency. However, the Swin Transformer's ability to extract local detail features of SAR images is weaker due to the slight differences among the SAR image sub-categories. The model's reliance on global semantic information limits its representation ability, while distinguishable detail features are crucial for SAR image classification tasks. The SAR images have prominent fine-grained characteristics, so it is necessary to conduct global modeling to obtain the long-distance dependence relationship of the SAR images. However, SAR images have low resolution and blurred edge texture details, which make it difficult to distinguish SAR targets from the background when only modeling them globally. Therefore, it is necessary to acquire their local features to enhance their ability to capture subtle differences.