# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Ablation Experiments  
We conducted a series of ablation experiments on the RSICD, and the results are detailed in Table 3, which lists the changes in model performance under different experimental settings. We evaluated the impacts of spatial positional encoding, the local awareness mechanism, and their combination. The model was quantitatively assessed using FID and CLIP scores. The results indicate that incorporating spatial positional encoding (VQ-Diffusion_SP) and the local awareness mechanism (VQ-Diffusion_LMA) effectively enhances model performance. The combination of both (RSVQ-Diffusion) yielded the most significant improvements. Specifically, the data in Table 3 demonstrate the improvements in image generation quality for each setting, further validating the effectiveness of these strategies in enhancing the performance of the VQ-Diffusion model. Using the VQ-Diffusion model as a baseline, it can be observed that the VQ-Diffusion_SP model shows a significant reduction in FID compared to the baseline model. The VQ-Diffusion_LMA model achieves an even greater reduction, while the proposed improved model, RSVQ-Diffusion, achieves the best reduction in FID relative to the original model. Regarding the CLIP text-image matching score, although the VQDM_SP and VQDM_LMA models exhibit similar performance, the RSVQ-Diffusion model shows a noticeable increase in this metric. Additionally, all three improved models show an increase in IS evaluation metrics compared to the baseline model. Although the IS improvement of RSVQ-Diffusion is not as large as that of VQ-Diffusion_LMA, considering the overall performance across other metrics, the RSVQ-Diffusion model stands out with its outstanding overall effectiveness. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Epoch & 100 \\ Batch size & 4 \\ Max learning rate & \(0.3\times 10^{-6}\) \\ Min learning rate & \(1.0\times 10^{-6}\) \\ Optimizer & AdamW \\ Weight Decay & \(4.5\times 10^{-2}\) \\ Learning Rate Scheduler & Reduce LR On Plateau With Warmup \\ Input Image Size & (256, 256) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training parameter setting. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **VQ-Diffusion** & **VQ-Diffusion\_SP** & \
\begin{tabular}{c} **VQ-Diffusion** \\ _LMA_ \\ \end{tabular} &
\begin{tabular}{c} **RSVQ-Diffusion** \\ _RSVQ-Diffusion_ \\ \end{tabular} \\ \hline SP & — & \(\surd\) & — & \(\surd\) \\ LMA & — & — & \(\surd\) & \(\surd\) \\ \hline FID\(\downarrow\) & 96.68 & 93.46 & 92.19 & 90.36 \\ CLIP\(\uparrow\) & 26.91 & 26.80 & 26.80 & 27.22 \\ IS\(\uparrow\) & 7.11 & 7.94 & 7.35 & 7.24 \\ \hline \hline \end{tabular} \(\surd\) means the module, or trick in the first column is used in the corresponding model.  
\end{table} 
Table 3: Ablation experiment results. Figure 5 presents eight groups of text descriptions (a)-(h) and their corresponding generated images. The improvements made to the model to account for the spatial characteristics of remote sensing images, as well as the introduction of the local awareness mechanism, have significantly enhanced the image generation quality. The RSVQ-Diffusion model demonstrates high-resolution and more stable image generation capabilities. Specifically, for group (a), the text description is \"This theme park has a lake and some buildings near the river\". The VQ-Diffusion model fails to capture the keyword \"river\". The images generated by the VQ-Diffusion_SP and VQ-Diffusion_LMA models show enhanced spatial positioning and details. The RSVQ-Diffusion model effectively generates images that include buildings, a lake, and a river, with reasonable ground features and clear boundaries. The differences from the real images highlight their diversity. For group (b), the text keywords are \"building\", \"river\", \"tree\", and \"overpass\". The  
Figure 5: Ablation experiment results.