# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 5 Conclusions  
The proposed RSVQ-Diffusion model achieves high-quality remote sensing image generation that aligns well with input semantic information. By incorporating the spatial characteristics of remote sensing images into the diffusion image decoder and employing the sequential local perception mechanism integrated with the Transformer architecture, the RSVQ-Diffusion model effectively generates target remote sensing images based on the input text. The generated images exhibit clear local details, reasonable spatial structure, and closely align with remote sensing image characteristics, achieving both controllability and realism. Additionally, we conducted a comprehensive comparative analysis to evaluate the impact of various improvements on the model's image generation quality. The experimental results indicate that the RSVQ-Diffusion model outperforms other models across various evaluation metrics and in terms of generated image quality, such as detail richness and reduced distortion and deformation. The improvement in FID scores demonstrates higher realism and closer resemblance to real images. The increase in CLIP scores reflects better alignment of generated images with input text, and the enhancement in ISs highlights superior image diversity. This study extends the application of diffusion models and Transformer architectures to the field of remote sensing imagery, broadening the research scope of remote sensing image generation methods. The RSVQ-Diffusion model generates remote sensing images with high fidelity, spatial consistency, and rich details, which can enhance data for remote sensing image processing tasks, simulate specific scenarios, and provide robust support for fields such as environmental monitoring, urban planning, and agricultural management.  
Although the proposed methods in this study have significantly improved the realism of remote sensing image generation and text-image matching accuracy, the existing remote sensing datasets lack extensively labeled text data, making manual labeling difficult and limiting the variety of generated images. There is room for further improvement in the model's generation capabilities and performance. In future work, we will focus on two main aspects: First, we aim to collect and construct diverse, high-resolution remote sensing image datasets to support large-scale generation tasks. Second, we will conduct in-depth research into Transformer architecture variants to optimize computational efficiency. This will enable us to improve training efficiency and inference speed, all while maintaining robust model performance. Lastly, by incorporating control conditions, such as metadata and geolocation information, and utilizing prior information like time and weather conditions, we aim to generate more informative remote sensing images. This will enhance the model's ability to generate realistic images, further improving its practical applications.  
Conceptualization, Y.F. and X.J.; methodology, X.G. and F.W.; software, X.G. and Y.F.; validation, X.G., Y.Z., T.F., C.L. and J.P.; formal analysis, X.G. and F.W.; resources, X.J.; data curation, X.G. and T.F.; writing--original draft preparation, X.G. and Y.F.; writing--review and editing, X.G. and F.W.; project administration, X.J.; funding acquisition, X.J. and F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Development Plan Project of Jilin Province, China, grant number 20220101168 JC, as well as funded by the National Key R&D Program of China, grant number 2022 YFB3902300, and funded by the National Natural Science Foundation of China, grant number 62371352.  
Not applicable.  
The data presented in this study are available on request from the corresponding author (the data are not publicly available due to privacy).  
The authors declare no conflicts of interest.