# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 4 Discussion
### Parameter Sensitivity Analysis  
CTAFNet contains two hyperparameters, i.e., the head numbers in MSA and the number of channels. To understand the sensitivity of CTAFNet to the hyperparameters, we tested the performance of different configurations in HSI classification. Table 9 shows the impact of head numbers in MSA on CTAFNet performance. With the increase in head numbers, the two performance indicators increase first and then decrease. This tendency is consistent across datasets. Table 10 shows the impact of different channel numbers on the performance of CTAFNet. The results show that, with the increase in channel numbers, the performance of the model also increases first and then decreases. This is possibly because  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Class** & **UNet** & **DeepLab v3+** & **SegFormer** & **Swin-UNet** & **UNet-m** & **SS3 FCN** & **ENLFCN** & **SSDGL** & **FSSANet** & **CTAFNet** \\ \hline \hline Acer negundo & 68.54 & 52.18 & 14.18 & 63.24 & 72.43 & 85.71 & 88.25 & 20.27 & 49.43 & 89.29 \\ Willow & 85.62 & 77.04 & 56.18 & 70.77 & 85.10 & 91.62 & 98.05 & 43.78 & 72.67 & 97.74 \\ Elm & 79.88 & 57.30 & 0.00 & 62.33 & 59.82 & 82.54 & 86.08 & 16.71 & 45.42 & 92.53 \\ Paddy & 94.82 & 97.62 & 89.89 & 93.76 & 97.05 & 96.68 & 98.95 & 95.53 & 95.76 & 99.20 \\ Sophora japonica & 73.32 & 78.77 & 35.37 & 70.00 & 70.31 & 91.88 & 90.62 & 47.58 & 52.18 & 91.31 \\ Faxaxinus chients & 80.43 & 77.90 & 34.64 & 49.20 & 77.37 & 91.14 & 93.15 & 41.71 & 74.34 & 85.19 \\ Goldenrain tree & 99.59 & 93.01 & 7.80 & 87.24 & 74.96 & 97.43 & 99.86 & 50.12 & 97.93 & 99.52 \\ Waters & 96.95 & 94.97 & 89.31 & 88.36 & 95.54 & 94.83 & 94.14 & 87.16 & 91.59 & 95.11 \\ Bara ground & 93.60 & 96.37 & 76.12 & 93.02 & 91.82 & 98.88 & 95.36 & 85.76 & 85.60 & 94.39 \\ Stubble & 87.53 & 98.01 & 82.62 & 95.13 & 97.72 & 99.56 & 98.17 & 88.09 & 97.36 & 99.96 \\ Corn & 58.02 & 56.89 & 21.06 & 34.57 & 56.34 & 65.95 & 77.61 & 8.97 & 61.65 & 80.21 \\ Pyrus & 76.00 & 80.09 & 58.47 & 61.74 & 68.51 & 86.90 & 86.94 & 48.97 & 65.91 & 93.86 \\ Soybean & 8.52 & 23.32 & 0.00 & 13.01 & 9.57 & 19.71 & 9.85 & 4.91 & 9.50 & 34.36 \\ Poplar & 74.69 & 71.24 & 28.68 & 59.29 & 69.95 & 72.20 & 70.30 & 28.48 & 48.72 & 78.53 \\ Vegetable field & 31.12 & 32.62 & 12.56 & 15.03 & 54.24 & 36.28 & 44.92 & 21.25 & 25.99 & 53.72 \\ Grass & 73.27 & 68.16 & 40.42 & 61.54 & 67.21 & 84.69 & 88.61 & 39.88 & 67.36 & 92.90 \\ Peach & 78.26 & 78.22 & 39.09 & 51.83 & 81.30 & 80.58 & 79.71 & 43.56 & 49.74 & 95.99 \\ Building & 83.70 & 83.49 & 76.26 & 60.84 & 88.33 & 88.25 & 90.83 & 67.33 & 64.61 & 89.38 \\ \hline pixel acc. & 87.29 & 87.52 & 66.58 & 79.63 & 86.17 & 93.79 & 94.37 & 64.91 & 79.38 & 96.17 \\ mIoU & 74.66 & 73.18 & 42.37 & 62.83 & 73.20 & 81.49 & 82.86 & 46.67 & 64.21 & 86.84 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification results for the Xiongan test set.  
the complexity of model increases together with the head numbers in MSA and the number of channels, and overfitting problem may occur when the model becomes too complex. When the head numbers of MSA is set to 2, and the model channel number is set to 96, CTAFNet performs best on the three datasets. Since the optimal hyperparameters are constants and no additional parameter tuning is needed, and the proposed CTAFNet is highly applicable in different scenes.