## 1 Introduction
Moreover, the extensive volume and intricacy of HSI data render traditional deep learning models computationally suboptimal, particularly in long-range modeling, where an excess of irrelevant tokens results in computational resource squandering. Hence, a model adept at processing extensive HSI data efficiently is crucial. To overcome these hurdles, the proposed neural models should embody the following attributes. Firstly, they should integrate a global self-attention mechanism to encapsulate global spectral attributes, reinforce relationships within spectral channels, and augment the model's spatial awareness. Secondly, the deployment of a group-sensitive pixel grouping mapping (PGM) module should facilitate the assignment of pixels to distinct groups, enabling the global self-attention mechanism to function within these clusters, thereby reducing computational intricacy and boosting model performance. Thirdly, a mechanism for selective perception (Sensitivity Selection Framework, SSF) should be implemented to dynamically identify tokens most pertinent to the classification task, lessening distraction from extraneous information, enhancing the model's representational proficiency, and enriching local representation through multi-scale feature selection. Fourthly, by merging global self-attention with local features, the model should adeptly represent both the global and local attributes of HSI data, enhancing classification accuracy. Finally, a streamlined computational framework should be devised to curtail resource wastage and amplify the model's efficiency in handling large-scale HSI data. In essence, the classification of hyperspectral images calls for neural models that are adept at managing the sophistication of spectral features, the synthesis of global and local attributes, computational efficiency, and the capturing of multi-scale features. These models should incorporate global self-attention, group-sensitive pixel grouping mapping, selective perception frameworks, the fusion of global and local features, and an optimized computational structure. Convolutional Neural Networks (CNNs) are adept at extracting spectral and spatial characteristics from hyperspectral images, leveraging their localized receptive fields and weight-sharing strategies [4]. This approach allows CNNs to discern meaningful local features from hyperspectral data, thereby enhancing classification precision. However, the use of small convolutional kernels hinders the capture of comprehensive global information, which can impede the efficacy of CNNs in classifying hyperspectral imagery. Vision Transformers (ViTs), on the other hand, excel in extracting global dependencies through their Multi-Head Self-Attention (MHSA) mechanism. The MHSA enables the model to process the image by considering all pixels, thus more effectively encapsulating global attributes. Despite this, ViTs' performance in hyperspectral image (HSI) classification is constrained by the absence of specialized optimization. The high dimensionality and intricate spectral nature of hyperspectral images necessitate particular model configurations and parameter tuning for effective processing. Consequently, the development of tailored ViT models for HSI is a pivotal area of contemporary research [5]. Scholars are examining the integration of CNNs' and ViTs' strengths to create hybrid models capable of extracting both local and global features. Furthermore, they are investigating ways to refine the architecture and training procedures of ViTs to bolster their classification capabilities in HSI tasks. These endeavors not only advance the analytical prowess of remote sensing data but also introduce novel insights and techniques for image processing across various domains. Hyperspectral images (HSI) are distinguished by their three-dimensional structure, encapsulating both spatial and spectral data, rendering three-dimensional Convolutional Neural Networks (3D CNNs), a potent instrument for their analysis.