## 1 Introduction
[34] devised a hybrid network incorporating a Transformer to capture long-range dependencies and a CNN to extract local information. [PERSON] et al. [35] formulated token sequences with multi-distance information for network training by exploring a novel token structure that fused convolution-extracted spatial features with original spectral information, enabling the interchange of local and global features. In the domain of hyperspectral image classification, [PERSON] et al. [36] introduced a multidimensional spectral transformation method grounded in channel correlation. They decreased input data dimensionality by utilizing input masks to extract pertinent spectral information while discarding irrelevant data. Furthermore, they integrated channel correlation blocks to capture correlations across diverse spectral channels, augmenting the model's recognition capabilities. [PERSON] et al. [37] implemented principal component analysis for dimensionality reduction on hyperspectral data, followed by leveraging spectral-spatial kernels to extract shallow features, which were then fed into a modified re-attention mechanism ViT model. Similarly, [PERSON] et al. [38] integrated 3D convolution for dimensionality reduction and superficial local feature extraction, followed by deep global feature extraction through a spectral-spatial Transformer module. Most recently, [PERSON] et al. [39] explicitly extracted local information from early layers, and utilized a Transformer to capture long-range dependencies in deeper layers, and introduced a lightweight Transformer network featuring a dual-branch multi-head self-attention module to address memory and computational efficiency concerns. While parallel and serial structures have propelled the integration of CNNs and the Transformer forward, mere concatenation of the two has not optimally harnessed their respective strengths. Conversely, models like FusionNet proposed by [PERSON] et al. [40] more effectively integrate the CNN and Transformer, achieving comprehensive utilization of hyperspectral image features through a fusion of serial and parallel mechanisms. [PERSON] et al. [41] established a dual-branch structure with a CNN and Transformer operating in parallel, introducing a feature interaction module to extract both global and local hyperspectral image features, facilitating bidirectional information exchange between the two. Ultimately, they make classification predictions through a multi-layer feature fusion module. This enhanced model adeptly combines the strengths of CNNs and Transformers to advance hyperspectral classification significantly and enhance accuracy. Although existing methods have pro gressed in hyperspectral classification, the fusion of the CNN and Transformer remains a formidable challenge. Further exploration of more effective fusion strategies is imperative to maximize the advantages of both and bolster classification performance. Therefore, devising fusion models that are better suited for hyperspectral classification, and adept at efficiently extracting local-global spatial-spectral features from hyperspectral images while considering model complexity, remains a formidable challenge. Inspired by the previously mentioned research and the agent attention method [42] this work introduces the dual-branch multi-granularity convolutional cross-substitution Transformer (DMCCT) model. The model adopts a dual-branch structure to enhance the fusion of the Transformer and CNN, enabling distinct extraction of spectral-spatial features and ultimately facilitating hyperspectral image classification through the integration of these features. By employing a dual-branch structure for separate spectral-spatial feature extraction, the model effectively tackles information loss and interference that may occur during simultaneous feature processing. The inclusion of multi-granularity encoding helps overcome Transformer limitations in capturing multi-granularity local features, promoting global communication of spectral and spatial features at various scales.