## 4 Discussion  
In the field of remote sensing image processing, we have developed a proficient hyperspectral image classification algorithm that reduces feature divergence in the feature extraction stage by incorporating a PGM module. This algorithm, by merging global and local features, not only enhances feature representation but also enables comprehensive extraction of these features. To validate the effectiveness of our method, we carried out extensive comparative experiments with state-of-the-art (SOTA) techniques on various hyperspectral images. The results indicate that transformer-based methods show significant promise in handling complex datasets, with our algorithm demonstrating outstanding accuracy and predictive classification capabilities in detailed remote sensing missions. Additionally, our algorithm excels in managing sensor data, ensuring the optimal use of hyperspectral sensors. It facilitates the smooth integration of sensor data flows, broadening its utility in environmental monitoring contexts, particularly in applications like forest health monitoring where accurate and timely data collection is crucial. The algorithm's proficiency in dealing with intricate sensor data streams guarantees that vital information is retained within the vast data produced by contemporary hyperspectral sensors. These findings conclusively prove that our algorithm has high efficacy and robust potential for sophisticated remote sensing data analysis, positioning it as an indispensable tool for sensor-based environmental and agricultural applications.  
In this study, we conducted a comprehensive evaluation and comparison of various advanced hyperspectral image HSI classification methods. Through the visual comparative analysis from Figures 2-6, the following conclusions can be drawn. The panels within the figures represent different classification components, including the original image (a) and label map (b), and a range of advanced methods, such as AB-LSTM (c), 3D-CNN (d), SPEFORMER (e), SFFT (f), M3D-DCNN (g), DFFN (h), RSSAN (i), and our proposed method (j). In Figure 2, SFFT (f), RSSAN (i), and our proposed method (j) demonstrate promising performance, indicating these methods have advantages in capturing the complex features of HSI data. In contrast, Figure 3 shows that 3D-CNN (d) and SPEFORMER (e) perform less accurately, being more prone to misclassification. Figure 4 further confirms that SSFTT (f) and our proposed method (j) are the top performers among all the compared methods. Lastly, Figures 5 and 6 highlight the superiority of our proposed method (j) in handling the edge regions of vegetation, particularly in delineating the details of classification boundaries, showing higher robustness and accuracy.  
We believe that this approach to data division is beneficial for enhancing the model's generalization ability in real-world applications.  
When analyzing attention-based models, especially considering that the latest solutions such as the Digital Layer Multi-Attention Networks (DLMA-Net) are the focus of our future research, DLMA-Net effectively enhances the performance of hyperspectral image classification through key components like multi-attention mechanisms, feature transfer, and digital layer representation. Compared to existing attention-based models, DLMA-Net has significant advantages in multi-scale feature extraction, dynamic feature selection, and digital layer representation, providing a powerful deep learning solution for the field of hyperspectral image processing. This is the direction of our future research [15].