## 1 Introduction
To prevent information loss during layer propagation, it uses cross-layer skip connections to adaptively fuse residuals across layers. Extended Vision Transformer (ExViT) [30] is a new framework based on multi-modal transformation that extends traditional ViT with minimal changes. Unlike traditional linear projections, ExViT is built on a lightweight separable backbone, extracting features of image patches through local and global perception in both spatial and channel dimensions. To enhance information exchange between multi-modalities, an attention fusion module is designed, which improves the model's discriminative ability by integrating attention at different stages. In ref. [31], considering that existing CNN-based models cannot sufficiently utilize the sequence attributes of spectral features, [PERSON] et al. embedded the convolution process into the changed structure of the transformer. MFT provides multi-head cross-patch attention for land cover classification tasks, which also utilizes other complementary information to achieve better generalization. Although the above methods based on transformers can obtain global features to increase accuracy effectively, these methods come with notable limitations, including high demands on memory and computational resources. Among numerous DL models, the CNN has demonstrated its prominent advantage of spectral-spatial characteristic extraction. A CNN has three main specialties: (1) the local receptive field, (2) the sharing of weights, and (3) the multiple convolution kernel. However, a CNN also has some problems that can be further optimized; particularly in the pooling layer, a significant amount of valuable information can be lost and the connection between the global and the local features may be overlooked. In CNN-based methods, the traditional method of using a 1D CNN [32] alone can capture spectral features from each pixel of HSI, but spatial information is not adopted. An HSI classification approach based on the structure of only utilizing a 2D CNN is called the spatial-based classification method, which extracts spatial features following a similar approach to traditional RGB images, which brings an inevitable weakness caused by ignoring the unified spectral-spatial attributes of the hypercube [33; 34]. Based on this, the appearance of the 3D CNN successfully captures the integrated spectral-spatial features across various spectral bands. For example, in [35], [PERSON] et al. propose a 3D dense convolutional network with a spectral-wise attention mechanism (MSDN-SA), which simultaneously captures the spectral-spatial features at multiple scales via 3D dilated convolutions. Despite experimental evidence from HSI public datasets showing that utilizing 3D CNNs enhances HSI classification performance, the increased computational complexity of deep 3D CNNs results in suboptimal outcomes for classes with analogous textures over multiple spectral bands. Therefore, to alleviate the mentioned issue, gradually integrating 2D and 3D CNNs with diverse kernel sizes has been paid more attention recently, which contributes to capturing more abstract joint spectral-spatial features, ultimately making the network strong enough. [PERSON] et al. leveraged the profound feature extraction capabilities of 3D CNNs for HSI classification and achieved better performance [36]. In ref. [37], ESFNet provided a 3D convolutional neural network based on the fusion of different spectral strides (SSFCNN), which can handle weighted spectral data to facilitate the efficient learning of spectral characteristics. The model optimizes the preservation of spectral continuity, thereby enhancing classification accuracy. The attention mechanism is also incorporated to enhance the feature matrices, optimizing the differentiation of the extracted features. In [38], the key model is the spectral-spatial domain attention network (SSDA), designed for few-shot HSI classification.