## 1 Introduction
Three-dimensional CNNs are capable of concurrently capturing temporal and spatial features, extracting rich spatial and spectral information from HSI. Yet, despite their strong performance in HSI classification, 3D CNNs still face challenges in capturing global interdependencies and emphasizing pivotal features [6]. To tackle these issues, researchers are introducing various methods that integrate attention mechanisms from Vision Transformers (ViT) into conventional CNN designs. These attention modules can bolster the model's capacity to discern correlations between spatial and spectral features in HSI. The attention mechanism empowers the model to dynamically concentrate on salient aspects of the input data, thereby enhancing feature extraction efficiency and classification accuracy. However, these attention-centric approaches, while promising for HSI classification, are fundamentally rooted in convolution, and thus, they are bounded by the localized nature of convolutional kernels, which can hinder the full expression of long-range spatial-spectral interactions. This indicates that while the attention mechanism aids in focusing on key data features, the inherent locality of convolution operations still restricts the model's grasp of global context. To surmount this barrier, researchers are delving into more innovative techniques, such as incorporating global pooling layers, expanding receptive fields, or devising new network architectures to better encapsulate long-range dependencies. Additionally, efforts are being directed towards the creation of fully attention-based models in the hope of effectively processing HSI data without reliance on convolutional operations. These research initiatives aim to further refine the classification of hyperspectral images and furnish more potent tools for future remote sensing data analysis. In the realm of high-dimensional data processing, scholars are persistently investigating and crafting advanced deep learning architectures to adeptly extract pivotal characteristics. [PERSON] and colleagues introduced the Residual Spectral-Spatial Attention Network (RSSAN) [7], an innovation that merges residual learning with spectral and spatial attention mechanisms. This harmonious integration is designed to effectively encapsulate intricate features within hyperspectral data. Residual learning facilitates the training of more profound networks by establishing direct pathways within the network architecture, while the spectral and spatial attention mechanisms concentrate on the data's spectral and spatial aspects individually, thereby enhancing the precision of feature extraction. Three-dimensional Convolutional Neural Networks (CNN3D) [8] are adept at feature extraction across three dimensions, proving invaluable for capturing the spatial configurations and temporal changes within data. This architectural design is particularly adept at handling hyperspectral data, as it concurrently processes both spectral and spatial dimensions. The Multi-scale 3D Deep Convolutional Neural Network (M3D-DCNN) is a deep learning framework designed for hyperspectral image (HSI) classification that effectively extracts spatial and spectral details by integrating multi-scale feature extraction with 3D convolutional processes, thereby boosting classification efficacy. The incorporation of 3D convolutional layers enables the model to more adeptly manage the three-dimensional nature of hyperspectral images, enhancing both the efficiency and accuracy of feature extraction. Furthermore, the M3D-DCNN amplifies its representational power through a multi-scale feature fusion module that synthesizes features across various scales, thereby refining the model's classification accuracy. This strategy for multi-scale feature fusion effectively amalgamates feature information across different scales, leading to an improvement in the model's classification precision [9].