## 6 Conclusions and Future Work  
This paper proposed Hyper-CycleGAN, which is an innovative adversarial neural network architecture designed specifically for hyperspectral data transformation. Utilizing the bidirectional mapping capabilities of CycleGAN, Hyper-CycleGAN ensured a consistent data distribution between the source and target hyperspectral data, facilitating efficient domain-wide reconstruction. The integration of multi-scale attention mechanisms significantly enhanced the generator's ability to discern subtle spectral relationships, prioritize important features, and account for spatial distributions, leading to improved quality and fidelity of the hyperspectral data. Moreover, we developed a tool for evaluating hyperspectral data based on the ResNet architecture, enabling precise classification and robust performance in hyperspectral data analysis tasks. Overall, our proposed Hyper-CycleGAN architecture outperformed traditional methods, as demonstrated by its superior classification capabilities in various hyperspectral data analysis scenarios.  
For future work, we aim to explore further enhancements to increase the efficiency and effectiveness of hyperspectral data transformation through advanced techniques from other fields, particularly reinforcement learning and evolutionary algorithms. Reinforcement learning approaches could enhance our framework through mechanisms like deep Q-networks that dynamically adjust attention weights based on generated data quality, enabling the model to learn optimal attention patterns across spectral bands. Policy gradient methods could optimize the generator's transformation strategy in real time, helping the model adapt to variations in input data characteristics, such as seasonal changes in remote sensing applications without requiring retraining. Complementarily, evolutionary algorithms could be implemented to evolve optimal network architectures for the generator and discriminator components. A coevolutionary approach could simultaneously optimize both architectures, leading to more robust adversarial training. This would involve fitness functions incorporating both spectral fidelity measures and classification accuracy, ensuring that evolved architectures maintain high-quality transformations. Future research will also focus on hybrid approaches combining reinforcement learning for parameter optimization with evolutionary algorithms for architecture search, addressing challenges related to scalability and computational  
Figure 13: (**a**) Original discriminator loss training plot (pattern collapse in red box). (**b**) Discriminator training plot using WGAN-GP loss.  
efficiency, specifically tailored to hyperspectral data. This strategic approach aims to advance the field and foster applications in real-world scenarios, such as precision agriculture, environmental monitoring, and mineral exploration.  
Conceptualization, Y.H., K.P.S. and L.M.A.; methodology Y.H. and K.P.S.; software, Y.H.; investigation, Y.H. and K.P.S.; writing--original draft, Y.H., K.P.S. and L.M.A.; writing--review and editing, K.P.S., L.M.A., X.Z. and B.P.; supervision, K.P.S., X.Z. and B.P.; project administration, K.P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes](https://www.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes)]([https://www.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes](https://www.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes)) (accessed on 2 April 2025).  
The authors declare no conflicts of interest.