## 3 The Proposed Hyper-CycleGAN
By prioritizing these essential spectral features, the generator can accurately capture the fundamental characteristics of the hyperspectral data, leading to more precise synthesis outcomes. Figure 2: Detailed structure of the generator (G) and the discriminator (D) in the model. Moreover, integrating spatial contextual cues into the framework fosters a deeper comprehension of the spatial distribution of spectral information. This requires examining how spectral characteristics vary across different spatial locations within a scene. For instance, the spatial arrangement of vegetation, water bodies, or built structures may reveal unique spectral signatures that contribute to the scene's overall spectral composition. Incorporating spatial contextual information allows the generator to better situate spectral features within their spatial contexts, resulting in more realistic and contextually relevant synthesis outcomes. In summary, incorporating multi-scale attention modules significantly enhances the Hyper-CycleGAN framework's capability to synthesize high-quality hyperspectral data. This enhancement facilitates capturing the subtle relationships between spectral bands, prioritizing salient spectral features, and considering the spatial distribution of spectral information. Consequently, this advancement propels the state-of-the-art in hyperspectral data synthesis forward. Discriminator: The discriminator's role in hyperspectral data analysis is pivotal for distinguishing between real and synthetically generated hyperspectral data. It scrutinizes the data's features, identifying genuine hyperspectral characteristics in the actual data while pinpointing discrepancies in the generated data. This discernment capability renders the discriminator an integral component in the training regimen of the generator. In our research, we selected PatchGAN as the foundational architecture for the discriminator module, explicitly optimized for hyperspectral data examination. PatchGAN employs convolutional neural networks (CNNs) to impose specific penalties on N*N feature blocks. Within this architectural framework, CNNs serve as the terminal layers, methodically seg menting the input hyperspectral image into discrete blocks or patches. This segmentation facilitates the independent assessment of each patch, enabling a localized scrutiny of spectral features. By concentrating on smaller segments of the hyperspectral image, PatchGAN not only simplifies the model's complexity but also enables a more detailed analysis of localized image attributes. Such localized scrutiny significantly augments the framework's proficiency in capturing the subtle details and structural intricacies inherent to hyperspectral data. Consequently, integrating PatchGAN into our methodology substantially enhanced our ability to discern complex spectral patterns and spatial interrelations within hyperspectral images, thereby improving hyperspectral data analysis and interpretation. Furthermore, to address potential training challenges and augment overall performance in hyperspectral data analysis, we incorporated Wasserstein GAN with gradient penalty (WGAN-GP) into our discriminator architecture. WGAN-GP is instrumental in refining hyperspectral data synthesis by tackling specific challenges inherent to the generation process. The essence of WGAN-GP lies in the introduction of a gradient penalty term, which applies constraints to the gradient architecture of the generated hyperspectral samples. This constraint effectively moderates the gradient, thus stabilizing the training process and facilitating the generation of more realistic hyperspectral data. WGAN-GP is crucial for overcoming issues related to gradient instability, such as gradient explosion or vanishing gradients, which pose significant challenges in hyperspectral data synthesis. By managing the gradient, WGAN-GP ensures more consistent training dynamics, culminating in enhanced efficiency in data synthesis and superior capabilities in data reconstruction and denoising.