## 5 Results and Discussion
### Classification Performance
This indicates that the proposed method achieved a substantial performance improvement when addressing hyperspectral image classification tasks. It further demonstrates the effectiveness of the method. In addition, Hyper-CycleGAN exhibited superior performance compared to the other two models for this dataset, suggesting that the model architecture effectively utilized the information extracted from hyperspectral images, demonstrating enhanced generalization capabilities. Figures 7 and 8 show the final classification maps of the two datasets, which visually illustrate the classification results produced by the models. Each pixel in the classification map represents a specific land cover class or category predicted by Hyper-CycleGAN based on the spectral features detected in the hyperspectral image. By comparing this classification map with real labels, the consistency and accuracy of the model's classifications can be observed. This map demonstrates  
Figure 8: The color classification map of target pixels in EHangzhou and RPaviaHR: **(a)** target database ground truth and **(b)** the classification map obtained by the proposed method. a high degree of consistency with the ground truth labels, especially with respect to the spatial distribution of the vegetation class and ground/building class in Figure 8. The boundaries between the different ground/building classes were coherent and tightly aligned between the two representations, highlighting Hyper-CycleGAN's ability to accurately delineate ground features. Upon closer inspection, individual pixels precisely corresponded to the ground/building class predicted by Hyper-CycleGAN. The detected spectral features were very similar to those depicted in the ground truth, underscoring the model's proficiency in leveraging spectral information for accurate classification. In addition, these maps successfully captured subtle variations in ground features, such as vegetation density and urban morphology. These nuances were closely related to the corresponding features present in the real annotations, demonstrating the model's capacity to capture the intricate details inherent in hyperspectral imagery. Although Hyper-CycleGAN outperformed existing methods in classification on multiple datasets, certain limitations in the classification results remained. To further analyze these limitations, we present in Figure 7c several regions where classification errors occurred, followed by an in-depth exploration of the underlying causes. As can be seen from Figure 7, on the RPaviaU and DPaviaC datasets, the classification failures were primarily concentrated in regions with fuzzy boundaries, small areas, and noise interference. For example, in the boundary areas of different feature classes (e.g., meadows and bare soil), the model was prone to confusion due to the transitional changes in spectral features. In addition, some small areas (e.g., shadows and self-blocking bricks) exhibited deviations in classification results from the real labels due to their small size and complex spectral features. Meanwhile, some areas may be disturbed by noise (e.g., changes in lighting conditions or sensor errors), which hinders the model's ability to accurately extract features.