## 5 Discussion  
In this work, we proposed two aspects for HSI classification:  
1. To extract HSI image features, and strengthen the connection between adjacent pixels, we proposed a transformer-based architecture with a mask input. This improved network performance in training.
2. To enhance the classifier's ability to discriminate between the input features, we proposed the channel correlation block (CCB) module to enhance its ability to distinguish between similar features.  
Our proposed method shows that the transformer-based model can better extract global features than the CNN model, rather than just localized features. The correlation between different channels can better help the model distinguish HSI categories. We set up three CNN-based methods and one transformer-based method to conduct comparative experiments with our transformer-based method. The OA and Kappa coefficients of the latter two groups were higher than the former on the three datasets, proving the effectiveness of global feature classification.  
For the two modules proposed, ablation experiments were set up to analyse their effectiveness. It can be seen from the OA and Kappa indicators that the CCB module performed better than the input mask. This shows that the correlation of deep features can more effectively distinguish features and improve the discriminative ability of the classifiers. The input mask, being a priori information for feature extraction, can reduce invalid information extraction in model training and improve the classification accuracy. These two methods improve the feature extraction ability of the model at different levels. The two components can be added to the model at the same time, and they achieved the best accuracy on three datasets, outperforming in the other individual settings in the ablation experiment.  
For the input mask component, we found that the pixel correlation of each pixel in its various directions decreased significantly with distance. The correlation between features exceeding a certain distance threshold and the features of the point pixel was low, so the mask input aided the model to focus on the feature information of each pixel to improve feature discrimination.  
Regarding the channel correlation block component, we found that in the last stage of the transformer architecture, the self-attention module was used to strengthen the mutual discrimination between pixel features. The similarity between features can be computed, together with the self-attention module, to provide high-quality features for the classifier.  
As a method of processing at the beginning, the input mask received low-level features with spatial sparsity, resulting in instability in the different inputs. For example, compared with the baseline on the PU dataset, the classification accuracy decreased slightly for the model with the input mask. However, in general, adding the mask to an image effectively focussed in on high-value feature information and improved the accuracy of model classification. In contrast, the CCB module with deep features and dense information increased the similar feature extraction ability of the classifier.  
Figure 5: Classification maps of different classification methods on the PU dataset. (**a**) CNN-2D. (**b**) CNN-3D. (**c**) FCN-ELM. (**d**) Spectral Former. (**e**) Ours. (**f**) GroundTruth.