# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Comparison Experiments  
A comparative analysis was conducted between the RSVQ-Diffusion model and the state-of-the-art text-to-remote-sensing image generation model, Txt2 Img-MHN, using the RSICD test set. The evaluation employed FID, CLIP, and IS as the assessment metrics to quantitatively analyze the generated results. The comparative experiment results are shown in Table 4.  
It can be observed that the RSVQ-Diffusion model outperforms the Txt2 Img-MHN model in both metrics, and it also shows improvements compared to the VQ-Diffusion model. This indicates that the RSVQ-Diffusion model excels in remote sensing image generation quality, text-image matching, and diversity, meeting the practical application needs of the remote sensing image processing field. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **FID\(\downarrow\)** & **CLIP\(\uparrow\)** & **IS\(\uparrow\)** \\ \hline Txt2 Img-MHN (VQVAE) & 175.36 & 21.35 & 3.51 \\ Txt2 Img-MHN (VQGAN) & 102.44 & 20.27 & 5.99 \\ VQ-Diffusion & 96.68 & 26.92 & 7.11 \\ RSVQ-Diffusion (Ours) & 90.36 & 27.22 & 7.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison experiment results. Figure 6 showcases eight groups (a)-(h) of images generated by four models: Txt2 Img-MHN with pre-trained models VQVAE and VQGAN, VQ-Diffusion, and RSVQ-Diffusion. Visually, although the RSVQ-Diffusion model's generated images exhibit minor issues such as irregular marking lines on the football field in group (b) and missing demarcated areas in the bottom left of the baseball field in group (f), it outperforms the other three models in terms of ground feature details and boundaries, text-image alignment, and overall visual quality. The RSVQ-Diffusion model's images are closer to real images. For instance, in group (a), with the text \"A bridge is built over the river\", the image generated by the Txt2 Img-MHN (VQVAE) model has an unclear lower left edge of the bridge, making it difficult to discern ground features, and it fails to capture the local structural features of the image. The Txt2 Img-MHN (VQGAN) model's image shows a deformed and blurred bridge with no clear distinction from the background. The VQ-Diffusion model's image has relatively clear boundaries but shows deformation at the upper right edge of the bridge and distortion in the bridge area. The RSVQ-Diffusion model's image, however, presents a complete bridge structure with clear boundaries and shadows beneath the bridge, enhancing realism and spatial coherence. In group (b), the Txt2 Img-MHN models  
Figure 6: Comparison experiment results. The first column shows the real images from the test set. The second and third columns display the images generated by the Txt2 Img-MHN model under two different pre-trained models. The fourth column presents examples of images generated by the VQ-Diffusion model, and the fifth column shows the images generated by the RSVQ-Diffusion model. Each row represents the images generated by different models for the same input text. generate images with blurred and deformed stadium boundaries and indistinguishable details. The VQ-Diffusion model's stadium edge has an unreasonable shape, and the surrounding buildings are deformed. The RSVQ-Diffusion model generates a clear image of the stadium with a visible central circle and clear, reasonable boundaries. In group (c), the Txt2 Img-MHN models produce images with basic spatial structures but blurred details, while the VQ-Diffusion model's left black area has an unnatural color texture distribution. The RSVQ-Diffusion model generates images with a reasonably structured river and clear details. In group (d), the Txt2 Img-MHN models' images have blurred building boundaries and overlapped ground features. The VQ-Diffusion model's image shows deformed and distorted building boundaries, while the RSVQ-Diffusion model generates images with neatly arranged buildings and clear boundaries, displaying visually realistic images.