## 4 Results
### Experimental Results and Analysis  
Tables 2-4 present the experimental results of the proposed method and six typical comparison methods for classification on the three distinct datasets in this study. It can be seen from the above table that the traditional machine learning method based on SVM has a general effect on hyperspectral image classification, and the overall accuracy does not reach 90% in the case of training samples of this proportion, which also shows that the effect of traditional machine learning methods is limited in dealing with high-dimensional hyperspectral data. On the other hand, CNN-based DBDA and SSRN can obtain better classification results by increasing the network depth and wide receptive field with the support of residual connection. However, the CS2 DT method based on Transformer has only a general effect on classification accuracy due to the lack of extraction of local detail information. The G_A and MSTViT methods based on the combination of CNNs and Transformers can not only better capture global features but also obtain locally detailed features so that the classification effect of deep networks with a CNN can be achieved under shallow network structure. Overall, the proposed method better integrates the respective strengths of CNNs and Transformers and achieves the best classification results. In particular, in the case of percentage-based sampling, the proposed method shows strong robustness when the training data for some categories are small due to the imbalance in the number of labeled samples. In the PU dataset, due to the similar spectrum of class 3 and class 8, the six methods except the proposed method have poor effects in the distinction of these two categories. The spectrum of class 1 and class 7 are similar, and the SVM and CS2 DT methods are easy to misclassify in the classification. There is also a poor classification of class 4 and class 5 in the KSC dataset, which is also caused by the existence of similar spectra between class 4 and class 5. In the IP dataset, class 1, class 7, class 9, and class 16 have a relatively poor classification effect, which is due to insufficient training samples for these four categories due to the percentage sampling in this paper. In contrast, the proposed method shows the best classification performance for phenomena such as similar spectra for different surface covers and under insufficient training samples. Qualitatively, Figures 6-8 illustrate the classification outcomes of the various methods across the datasets. Observation of these figures indicates that methods with weaker classification results exhibit more noise in their outputs. While SVM and CS2 DT showed some classification success, they struggled with edge processing and detail recognition. Conversely, models like SSRN, DBDA, G_A, and MSTViT achieved overall good results with room for improvement in specific regions. The proposed method outperformed othersin detail processing, accuracy, and overall classification map coherence, presenting results closest to the ground truth across the datasets. In summary, the proposed method demonstrated superior classification performance compared to the alternative models, showing excellence in detail processing, global accuracy, and robustness across varying datasets.