## 2 Research Area and Data
### The Proposed Methodology  
Figure 1 depicts how the GSAT model apportions the full spectral range into discrete subchannels via the PGM module. The PGM module specifically carves up the spectral aspect of the hyperspectral imagery into a series of nonintersecting subchannels, with each harboring a segment of the spectral bands. This division enables the model to independently address the spatial-spectral characteristics of every subchannel, which in turn facilitates a more precise capture of spectral dimension locality. Within these subchannels, the PGM module leverages a global self-attention process to extract spatial-spectral features. In the PGM module's operation, self-attention is employed across each subchannel to gauge the interpixel relevance, thereby delineating spatial dimension relationships. This approach to feature extraction not only heightens the model's spatial awareness but also optimizes the feature extraction process in terms of both efficiency and precision. The SSF module selectively retains the most pertinent tokens by evaluating their association with the classification objective. It does so by utilizing a relevance matrix that encapsulates the relationship between each token and the classification goal, pinpointing tokens that exceed a predetermined threshold in this matrix. Tokens that meet this criterion are kept, while the remainder are omitted. This selective process minimizes the impact of extraneous data, thereby enhancing the model's capacity for representation. Additionally, the SSF module augments local representation by implementing a multi-scale feature selection approach. It engages in feature selection across diverse scales, thereby encapsulating feature details at multiple levels. This strategy for multi-scale feature selection is effective in capturing the intricate characteristics within hyperspectral imagery, which in turn boosts the accuracy of classification. Figure 1: Schematic diagram of the GSAT architecture for hyperspectral image classification tasks. Once the most relevant tokens are identified, the SSF module implements a mechanism for adjusting their weights dynamically. The module fine-tunes the weights of each token in accordance with its relevance, thereby amplifying the features that are beneficial for the classification task. This strategy for dynamic weight adjustment serves to further augment the model's representational capabilities and its classification efficacy. The combined efforts of the PGM and SSF modules enable the GSAT model to execute feature extraction with high efficiency. The PGM module contributes to the model's spatial acuity by segmenting the spectral range and extracting spatial-spectral features, while the SSF module minimizes the distraction of irrelevant information and boosts the model's representational strength by focusing on the most pertinent tokens, capturing features across multiple scales, and applying dynamic weight adjustments. This collaborative approach guarantees the robustness of feature extraction, enabling the model to more effectively encapsulate the intricate details within hyperspectral images. The GSAT model, with its streamlined feature extraction process, is capable of delivering precise classification of hyperspectral imagery. By leveraging the combined functionality of the PGM and SSF modules, the model extracts a wealth of spatial-spectral features and fortifies its representational capabilities through the mechanisms of dynamic selection and weighting. This fortified representation enables the model to more adeptly identify the nuanced features within hyperspectral images, thereby enhancing classification accuracy. Empirical findings demonstrate that the GSAT model exhibits superior performance over conventional deep learning methods across various established HSI dataset benchmarks. Within the context of this study, L denotes the adjustable number of layers, with a standard configuration of two layers.