## 5 Results and Discussion
### Qualitative Performance  
Our Hyper-CycleGAN implementation distinguishes itself from the original CycleGAN by incorporating a generator network structure with a multi-scale attention mechanism, thereby enabling the synthesis of detailed features in hyperspectral data. This specialized module captured both local and global features, thereby enhancing the reconstruction of structural texture details and improving the fidelity of synthesized hyperspectral data. By selectively attending to relevant spectral bands and spatial regions, the attention mechanism facilitated improved differentiation among various classes or categories present in the hyperspectral images. The adaptive nature of this mechanism ensured accurate capture of both fine-grained details and broader contextual information, yielding superior feature representations. Figure 9 illustrates the enhanced discriminative power achieved by our method. The features of a patch in the target scene were more specific after using the multi-scale attention layer compared to before using it, which contained more details, demonstrating the effectiveness of the multi-scale attention layer in capturing subtle variations in hyperspectral data. Additionally, the spatial distribution of spectral information was faithfully replicated, reflecting accurate mapping of spectral features across the hyperspectral domain. In addition to capturing more detailed features, the quality of the mappings generated by our Hyper-CycleGAN exhibited outstanding performance. Figure 10 illustrates the spectral maps for our dataset, comprising multiple categories, each characterized by distinct spectral features, including absorption peaks, valleys, and intensity distributions. These spectral maps viv  
Figure 9: Feature space mapping of the target scene. ations in spectral features across different categories, highlighting the importance of these differences in material or substance identification and analysis. Figure 11 displays the results of the hyperspectral spectrograms generated by our Hyper-CycleGAN for the fifth category, representing self-blocking brick spectrograms. Figure 12 displays the results of the hyperspectral spectrograms generated by our Hyper-CycleGAN for the third category, representing vegetation spectrograms. These spectrograms exhibit a remarkable level of fidelity and coherence compared with the original dataset. Through the integration of a multi-scale attention mechanism, our Hyper-CycleGAN demonstrated outstanding performance in capturing intricate spectral features, including subtle variations in reflectance and absorption patterns specific to self-blocking bricks. The synthesized spectrograms maintained a high level of clarity and realism, showcasing our model's effectiveness in preserving the unique spectral signatures of different materials. Additionally, the spatial distribution of spectral information was faithfully replicated, reflecting accurate mapping of spectral features across the hyperspectral domain. To thoroughly evaluate the performance of our method beyond classification accuracy, we utilized the mean structural similarity index measure (MSSIM) and mean peak signal-to-noise ratio (MPSNR) to measure the perceptual quality of the generated hyperspectral images. As shown in Table 5, Hyper-CycleGAN demonstrated superior performance in hyperspectral image generation, significantly surpassing the traditional CycleGAN. On the DPAviaC dataset, Hyper-CycleGAN achieved MPSNR and MSSIM values of 29.23 and 0.787, respectively, which are notably higher than the corresponding values of 28.317 and 0.735 achieved by CycleGAN. Similarly, on the RPAviaHR dataset, Hyper-CycleGAN achieved MPSNR and MSSIM values of 29.461 and 0.802, respectively, substantially outperforming CycleGAN's values of 28.342 and 0.729. The improved performance of Hyper-CycleGAN is attributed to its enhanced ability to model spectral and spatial features.