# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 5 Conclusions  
In this article, a novel CTAFNet is proposed for HSI classification. We designed a CTAF module to capture the local high-frequency features using a convolution module and extracts the global and sequential low-frequency information using a Transformer module. Afterwards, the local high-frequency and global low-frequency features are adaptively weighted and fused to provide a more robust and discriminative representation of the HSI data. An encoder-decoder structure was adopted in the CTAFNet to improve the flow of fused local-global information between different stages, thus ensuring the generalization ability of the model. Experimental results conducted on three large-scale challenging HSI datasets demonstrate that the proposed network is superior to nine state-of-the-art approaches. The developed adaptive feature fusion strategy can effectively overcome the \begin{table}
\begin{tabular}{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 Breccoli green weeds 1 & 99.14 & 0.00 & 16.63 & 77.59 & 97.06 & 100.00 & 100.00 & 9.49 & 100.00 & 100.00 \\ Breccoli green weeds 2 & 100.00 & 74.01 & 1.33 & 89.49 & 99.87 & 100.00 & 100.00 & 4.20 & 100.00 & 100.00 \\ Fallow & 95.41 & 11.29 & 39.90 & 82.89 & 100.00 & 85.28 & 85.04 & 17.39 & 83.23 & 100.00 \\ Fallow rough plow & 86.45 & 73.03 & 72.03 & 98.29 & 95.02 & 98.97 & 91.11 & 89.13 & 97.92 & 98.62 \\ Fallow smooth & 95.22 & 95.72 & 94.34 & 94.18 & 99.48 & 99.27 & 98.93 & 99.58 & 98.35 & 99.69 \\ Stubble & 100.00 & 67.84 & 64.73 & 94.63 & 99.56 & 99.85 & 100.00 & 94.87 & 96.76 & 100.00 \\ Celery & 99.71 & 99.86 & 7.71 & 97.29 & 100.00 & 99.28 & 100.00 & 23.18 & 97.57 & 100.00 \\ Grapes untrained & 38.36 & 37.28 & 40.66 & 61.13 & 65.99 & 72.46 & 75.94 & 18.15 & 66.20 & 75.06 \\ Soil vineyard develop & 98.82 & 83.38 & 83.81 & 99.88 & 95.67 & 99.71 & 99.88 & 97.85 & 97.76 & 99.51 \\ Corn senesed green weeds & 67.75 & 50.51 & 51.65 & 62.56 & 80.10 & 79.92 & 83.03 & 45.93 & 81.85 & 98.66 \\ Letreduce romaine 4 wk & 87.41 & 24.68 & 38.46 & 69.05 & 92.38 & 86.62 & 85.62 & 52.24 & 84.62 & 100.00 \\ Letreduce romaine 5 wk & 96.45 & 5.80 & 13.77 & 83.41 & 85.33 & 97.24 & 98.60 & 51.62 & 98.24 & 100.00 \\ Letreduce romaine 6 wk & 93.64 & 0.00 & 4.37 & 80.88 & 93.16 & 89.52 & 96.09 & 87.70 & 77.00 & 100.00 \\ Letreduce romaine 7 wk & 88.19 & 64.58 & 70.08 & 75.21 & 87.26 & 77.41 & 83.72 & 79.46 & 69.49 & 99.13 \\ Vinyard untrained & 56.43 & 0.00 & 31.97 & 64.59 & 67.97 & 66.49 & 67.11 & 17.89 & 54.25 & 60.48 \\ Vineyard vertical trellis & 18.59 & 0.00 & 0.00 & 82.10 & 0.00 & 91.86 & 88.28 & 0.00 & 78.49 & 100.00 \\ \hline pixel acc. & 85.70 & 63.44 & 61.24 & 88.72 & 90.99 & 92.95 & 93.55 & 59.84 & 90.44 & 94.62 \\ mIoU & 82.60 & 43.00 & 39.47 & 82.07 & 84.90 & 90.24 & 90.80 & 49.29 & 86.34 & 95.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of generalization performance on the Salinas dataset. limitations of the existing hybrid strategy and improve the accuracy of HSI classification. Our research provides promising methods for HSI classification and keen insights into the comprehensive performance differences between models. Conceptualization, J.L., H.X. and H.W. ; methodology, J.L. ; data curation, J.L. ; writing--original draft preparation, J.L. and Z.A. ; writing--review and editing, J.L., Z.A. and W.L. ; funding acquisition, H.X. and A.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (Nos. 41971406, 42071246, 42271470), the Natural Science Foundation of Hebei Province, China (No. D2021402007) and the Guangdong Basic and Applied Basic Research Foundation (No. 2022A15101586). Not applicable.