## 4 Experimental Results
### Classification Maps and Categorized Results  
In this section, to evaluate the classification performance of the proposed HDCDF, we present the detailed results of different methods under three datasets. Tables 4-6 and Figures 14-17 show the detailed accuracy and visual results of the HDCDF method compared with other methods. It can be seen from the experimental results that, although the model does not achieve optimal results in all categories, HDCDF performs the best in three indicators, OA, AA, and Kappa, with values of 95.73%, 96.51%, and 95.22% on the IP dataset, 96.5%, 97.33%, and 95.26% on the UP dataset, and 97.84%, 98.74%, and 95.46% on the SV dataset, showing a better classification ability on those categories and that other models do not perform well generally, compared with other networks. To be specific, it is obvious that the overall classification effect of the proposed model on the UP and SV datasets is better than that on the IP dataset. Although we only selected 0.5% training samples for these two datasets, there are still enough training samples for each category of them compared with the IP dataset. As a result, SpectralFormer and ExViT,  
Figure 14: Results of parameter analysis. (**a**) The Effect of B on OAs on three datasets; (**b**) the effect of P on OAs on three datasets. based on the transformer design, are sufficiently trained in that the final classification results are higher than other networks and closer to HDCDF. However, the number of training samples on the IP dataset is limited, so the SpectralFormer and ExViT networks have lower classification results (OAs: SpectralFormer: 91.13%; ExViT: 94.59%) than HDCDF, which also shows that HDCDF can still maintain excellent classification performance in the case of insufficient samples. This may be due to the double-branch feature fusion structure of HDCDF, which helps to extract more abundant features, and thus the training results are better than other models. From the IP dataset, the classification effect of the 3 DCNN model is not ideal as the model only relies on a 3D convolution operation; the extracted features are not as sufficient as DBMA with the introduction of an attention mechanism and LSSMA with the introduction of a residual module. In addition, we can see that the phenomenon of salt-and-pepper noise is severe in the 3 DCNN network classification figure. In contrast, the HDCDF classification far outperforms the SpectralFormer and ExViT networks. The SpectralFormer network pays more attention to the role of spectral features in classification due to the large number samples in the IP dataset, so the correlation between space and spectral features should be fully utilized to improve classification accuracy. HDCDF includes convolutions of different sizes and dimensions for residual connection and feature fusion, which can extract deeper features and reduce the number of training parameters. In the UP dataset, we find the sixth category in the middle of the picture; the 3 DCNN and DBMA models have many pixels identified as the second category. This may be due to the similarity of the spectral features of the two categories, and the model cannot accurately identify the extracted features. The network proposed in this paper achieves almost 100% in this category. This result also proves the superiority of HDCDF in extracting features, which makes the problem of large spatial differences in spectral features of HSI effectively solved.