## 4 Experiment
### Results  
In the quantitative analysis, we present the performance metrics of novel view synthesis for selected scenes in Table 3, which includes the PSNR and SSIM scores for our proposed method across various scenarios within the experimental dataset. Notably, our method exhibits superior performance over previous methods in terms of both the PSNR and SSIM metrics, indicating its effectiveness in generating high-quality synthesized views. However, a closer examination of the SSIM scores reveals a particular case where the SatelliteRF method on Scene 004 demonstrates a lower SSIM score compared to other NeRF-based approaches. Scene 004 is distinguished by its highly dynamic range and complex geometric structures, which pose significant challenges for depth estimation and texture rendering. The SatelliteRF method, while robust in handling various scenarios, might be less effective in accurately capturing the intricate details and maintaining texture consistency in such a complex environment. This discrepancy underscores the importance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **004** & **068** & **214** & **260** \\ \hline number of images & 11 & 19 & 24 & 17 \\ split & 9/2 & 17/2 & 21/3 & 15/2 \\ height ranges & \([-24,1]\) & \([-27,30]\) & \([-29,73]\) & \([-30,13]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset details. The table includes the number of images, train and test split, and height ranges. Figure 7: DFC 2019 dataset examples. The scene contains vegetation, urban buildings, water bodies, etc. of further refining the depth estimation and texture rendering components of SatelliteRF to enhance its adaptability to scenes with complex geometries and dynamic ranges. Figure 8 illustrates that, in the presence of varying illumination conditions typical of remote sensing scenarios, the original NeRF fails to accurately capture the scene's appearance. In contrast, S-NeRF, Sat-NeRF, and our proposed method are capable of effectively managing these changes in illumination. As shown in Figure 9, our method has significant improvements in image reconstruction quality and visual effects. Especially when dealing with complex scenes and detail-rich areas, our method can better capture the characteristics of the original scene and generate more realistic and natural images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **004** & **068** & **214** & **260** \\ \hline \multicolumn{5}{c}{**PSNR (dB)**} \\ \hline NeRF & 20.72 & 20.99 & 18.42 & 20.08 \\ \hline S-NeRF & 25.86 & 24.29 & 24.16 & 21.37 \\ \hline Sat-NeRF & 26.32 & 25.11 & 24.99 & 21.79 \\ \hline SatelliteRF (Ours) & **26.59** & **25.29** & **25.51** & **22.03** \\ \hline \multicolumn{5}{c}{**SSIM**} \\ \hline NeRF & 0.640 & 0.826 & 0.808 & 0.773 \\ \hline S-NeRF & 0.864 & 0.897 & 0.936 & 0.816 \\ \hline Sat-NeRF & **0.877** & 0.912 & 0.946 & 0.842 \\ \hline SatelliteRF (Ours) & 0.858 & **0.924** & **0.958** & **0.857** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Novel view synthesis metrics. We report image rendering metrics for test views of selected scenes. Figure 8: Visualization of novel view synthesis results on four scene data of the DFC 2019 dataset. To validate that our method is able to learn high-quality 3D models, we qualitatively compared the mesh models generated by SatelliteRF with those generated by lidar and S2P [1]. S2P won the championship in the 2016 IARPA Multi-View Stereoscopic 3D Mapping Challenge. As shown in Figure 10, although the model surface generated by S2P is smoother, the model obtained by SatelliteRF shows finer structural details. The unevenness of the surface of the satellite-generated model is mainly due to the fact that the NeRF is unable to accurately reach the surface during sampling and can only sample near the surface. This problem may be improved by introducing depth supervision or geometric constraints.