## 5 Results and Discussion
### Classification Performance  
In this study, we evaluated the classification performance of five different models on a hyperspectral dataset, including the traditional CycleGAN, CycleGAN combined with WGAN-GP, our proposed model, Hyper-CycleGAN, and the additional methods, 3-D-SRNet [41] and Cycle-AC-GAN [42]. The 3-D-SRNet method is a cross-scene hyperspectral image (HSI) classification algorithm based on pretraining and fine-tuning. We used overall accuracy (OA) and average accuracy (AA) as metrics. Table 3 presents the experimental results on the DPaviaC dataset, while Figure 6 shows the results averaged over 10 independent runs on the DPaviaC dataset. Table 4 presents the experimental results on the RPaviaHR dataset. Figure 7 shows the final classification map in the RPaviaU and DPaviaC datasets. Figure 8 shows the final classification map in the EHangzhou and RPaviaHR datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **OA (\%)** & **AA (\%)** \\ \hline
3-D-SRNet & 83.54 & 89.97 \\ \hline CycleGAN & 86.73 & 87.62 \\ \hline CycleGAN + WGAN-GP & 87.74 & 88.72 \\ \hline Cycle-AC-GAN [42] & 92.36 & 96.47 \\ \hline Hyper-CycleGAN & 94.67 & 97.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: OA and AA of each method on the RPaviaHR dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **OA (\%)** & **AA (\%)** \\ \hline
3-D-SRNet & 73.67 & 76.48 \\ \hline CycleGAN & 83.75 & 84.34 \\ \hline CycleGAN + WGAN-GP & 84.52 & 85.27 \\ \hline Cycle-AC-GAN [42] & 88.14 & 88.72 \\ \hline Hyper-CycleGAN & 90.02 & 92.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OA and AA of each method on the DPaviaC dataset. Figure 7: The color classification map of target pixels in RPaviaU and DPaviaC: (**a**) target database ground truth, (**b**) the classification map obtained by the proposed method, and (**c**) regions of classification failure. The superiority of adversarial neural networks in handling hyperspectral data is clear, with even the basic CycleGAN outperforming 3-D-SRNet by more than 10% in OA. This highlights the inherent capability of adversarial networks to effectively extract and leverage intricate spectral features for improved classification accuracy. Adversarial networks excel at learning complex representations directly from raw data, a critical advantage in hyperspectral data analysis. Their adaptability and feature extraction prowess make them ideally suited for tasks where traditional methods may struggle to capture subtle spectral patterns. Data analysis showed that Hyper-CycleGAN demonstrated significant improvements in OA and AA compared to the traditional CycleGAN model, with increases of approximately 6.27% and 8.53%, respectively. Compared to the CycleGAN + WGAN-GP model, Hyper-CycleGAN achieved improvements of approximately 5.50% and 7.60% in OA and AA, respectively. These results indicate a substantial performance enhancement of Hyper-CycleGAN in hyperspectral image classification tasks, particularly in terms of average accuracy, where its performance outperformed the other two models. While CycleGAN, as a traditional image transformation model, achieved certain classification performance on hyperspectral datasets, its accuracy was relatively low, primarily due to the model's failure to effectively capture key features in the spectral data. Although CycleGAN + WGAN-GP slightly improved performance by incorporating Wasserstein GAN and gradient penalty, the improvement was limited. In contrast, Hyper-CycleGAN, by integrating multi-scale attention mechanisms and WGAN-GP loss, significantly enhanced classification performance, representing a breakthrough in hyperspectral image classification tasks. Compared to Cycle-AC-GAN, which consists of a binary domain classifier and an auxiliary land cover classifier, our proposed Hyper-CycleGAN architecture also achieved significant improvements. The Hyper-CycleGAN achieved significant improvements amounting to about 6.27% in OA and 8.53% in AA.