## 1 Introduction
To address this issue, a novel attention-based neural network model known as the \"Transformer\" was introduced in 2017 and initially applied in natural language processing. The integration of Vision Transformer (ViT) successfully introduced Transformer into the realm of image processing, effectively capturing long-range dependencies in input images [26]. The successful deployment of ViT in computer vision has opened up new research avenues in the domain of remote sensing. Recent research leveraging ViT techniques has yielded significant advancements and explorations in hyperspectral image classification. [PERSON] et al. [27] introduced a method utilizing the Spectral Swin Transformer Network for hyperspectral image classification, offering a novel approach to hyperspectral imaging. [PERSON] et al. [28] developed a crop hyperspectral image classification technique based on Transformer by enhancing the Swin Transformer in the SegFormer encoder while retaining the SegFormer decoder. [PERSON] et al. [29] introduced Grouped Pixel Embedding to refine patch encoding and address feature dispersion in Multi-Head Self-Attention extraction, achieving spatial-spectral feature classification through the stacking of Transformers. [PERSON] et al. [30] adopted window-based self-attention and shifted window-based self-attention to learn local and global features, employing a dual-branch structure for separate extraction of spatial and spectral features. [PERSON] et al. [31] proposed a spatial-spectral transformation method incorporating cross-attention to interact spatial and spectral sequence branches for token classification, with a focus on fusing spatial-spectral information and enhancing the model through a weighted shared spatial-spectral cross-attention module to reduce computational complexity. These methodologies grounded in ViT and its modified versions emphasize global information while potentially disregardingfine-grained local features or implementing shifted window-based self-attention for local attention computation within and across windows, potentially resulting in overly intricate implementations. The importance of local details in spectral-spatial features becomes evident in the identification of material objects, particularly in pixel-wise hyperspectral image classification, where relying solely on global features extracted by ViT strategies may not suffice for capturing detailed classification features. To further exploit the local and global features present in images, researchers have extensively explored the combination of CNNs for local feature extraction with Transformer-based global feature extraction. Some scholars have adopted a parallel structure, where CNNs and the Transformer process different sections of input data independently, later combining their respective features. For instance, [PERSON] et al. [25] introduced the S2 Former network framework, comprising parallel branches of spatial and spectral transformers, along with a local feed-forward network to supplement local details. Similarly, [PERSON] et al. [32] utilized a parallel structure of a CNN and Transformer. They initially employed a series of parallel residual blocks to capture local spectral-spatial attributes in hyperspectral image patches, then implemented a dual-branch structure comprising a CNN and Transformer branches for separate modeling of local-global hyperspectral features. Other researchers have employed a serial structure, starting with a CNN for extracting local features from input data and subsequently transferring these features to a Transformer for global feature modeling and learning. For example, [PERSON] et al. [33] first generated feature representations of hyperspectral images using a Convolutional Autoencoder (CAE), then fed these features into a lightweight visual Transformer for classification. [PERSON] et al. [24] began by extracting spatial features with a CNN, later integrating an improved dense-connected Transformer to model sequential spectral relationships for classification tasks. In addition, [PERSON] et al.