## 3 Experiments and Discussion
### Classification Results  
To verify the performance of the model, several advanced HSI classification methods were selected for comparison with this model, including 3D-CNN [37], GCN [24], MiniGCN [22], HybridSN [18], DBDA [19], and MDGCN [27]. The number of training samples and test samples selected for each method were the same, and the hyperparameters were the same as in the original paper. The classification accuracies of the different methods on each dataset are shown in Tables 4-6 the best results in each class were bolded and the classification maps obtained by these methods are illustrated in Figures 6-8; the experimental results are the average of five experiments. It is worth noting that although we have minimized the risk of data leakage, the issue may still exist and affect the classification results. Table 4 shows the classification results of the different models on the IP dataset. The lack of training samples on the IP dataset and the imbalance in the number of samples from different categories made classification challenging, but our classification method obtained the best classification results. It can be observed that the classification accuracy obtained by the 3D-CNN was lower than other methods, which might have been due to the fact that the 3D-CNN had more parameters, but the number of training samples was small in this experiment. In addition, it did not take full advantage of the relationship information contained in the samples, which eventually led to poor classification results. HybridSN combines 3D-CNN layers with 2D-CNN layers, which has a stronger feature representation capability by combining spatial and spectral information and a lower number of parameters, but the accuracy was still lower in the case of a small number of samples. DBDA contains two branches to obtain spatial-spectral features, respectively, and introduces the attention mechanism and eventually achieved better classification results than HybridSN. The GCN-based classification method can generally obtain better classification results with a smaller number of samples. MiniGCN adds a convolutional branch and adopts a small batch strategy compared with GCN. MiniGCN achieved better classification results but did not take into account the different importance of different features. In contrast, the FCGN obtained the best classification results, which was greatly due to the design of two branches to obtain complementary features. The graph convolution branch based on superpixel segmentation can obtain large-scale irregular features of the image and the relationship between different nodes, reducing the classification error caused by noise. The convolutional neural network with the added attention mechanism can acquire regular image features at a small image scale and generate detailed edge features, which complements the smooth features acquired by the superpixel segmentation-based graph convolution branch to obtain better classification results on both large and small scales. The convolution branch was able to process the local fine area to obtain the detailed features of the image; due to the misclassification of pixels, the classification result of the convolution branch contained more noise. By fusing the features of the two branches, the influence of noise on the classification results was greatly reduced. In terms of running time, the FCGN had a