## 3 Results
### Comparative Analysis
#### 3.2.4 Match Point Precision Analysis  
To precisely evaluate the quality of tie points in multi-satellite imagery, by employing this method, the study assessed the performance of STSU-SAR-SIFT, SAR-SIFT, SIFT, SURF, SuperGlue, and RedFeat algorithms for dual and triple tie points on both the same and vertical orbits, using GF-3 and Sentinel-1 data. As the SIFT and SURF algorithms do not generate tie points on vertical orbits, Table 4 exclusively presents the RMSE measurements for the same orbits. Figures 9 and 10, respectively, display partial matching results of GF-3 and Sentinel-1 images. The analysis results from Table 4 are as follows:  
1. For both GF-3 and Sentinel-1 images, our proposed STSU-SAR-SIFT algorithm consistently achieved the lowest RMSE values. Even when compared with the state-of-the-art deep learning-based matching algorithms such as SuperGlue and RedFeat, our algorithm demonstrated distinct advantages and was significantly superior to SAR-SIFT. 2. Given the minimal variability in SAR images captured during the same orbital imaging period, all algorithms perform adequately in extracting tie points from the same orbit. However, SIFT, SURF, and AKAZE are more sensitive to noise, resulting in comparatively higher root mean square errors. Meanwhile, the two deep learning \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & **Different** & **RMSE** & **RMSE (Same** & **RMSE (Vertical** & **RMSE (Two-** & **RMSE (Three-** \\  & **Algorithms** & **(All)** & **Orbit)** & **Orbit)** & **Overlapping)** & **RMSE (Three-** \\ \hline \multirow{6}{*}{GF-3} & STSU-SAR-SIFT & 0.43 & 0.27 & 0.58 & 0.40 & 0.41 \\  & SAR-SIFT & 0.68 & 0.28 & 1.14 & 0.66 & 0.67 \\  & SIFT & 0.71 & 1.72 & None & 0.31 & None \\  & SURF & 0.75 & 1.79 & None & 0.35 & None \\  & AKAZE & 0.77 & 1.04 & None & 0.37 & None \\  & SuperGlue & 0.65 & 0.33 & 0.75 & 0.69 & 0.71 \\  & RedFeat & 0.58 & 0.31 & 0.67 & 0.58 & 0.64 \\ \hline \multirow{6}{*}{Sentinel-1} & STSU-SAR-SIFT & 1.27 & 0.59 & 2.36 & 1.37 & None \\  & SAR-SIFT & 1.87 & 0.37 & 2.77 & 1.87 & None \\ \cline{1-1}  & SIFT & None & None & None & None \\ \cline{1-1}  & SURF & None & None & None & None \\ \cline{1-1}  & AKAZE & None & 0.79 & None & 3.64 & None \\ \cline{1-1}  & SuperGlue & 1.61 & 0.74 & 2.56 & 1.65 & None \\ \cline{1-1}  & RedFeat & 1.54 & 0.62 & 2.54 & 1.63 & None \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE comparison of GF-3 and Sentinel-1 data. Figure 8: Feature point extraction results of Sentinel-1 data. based methods have shown excellent results across both datasets. Nevertheless, according to the data in the table, STSU-SAR-SIFT exhibits the best performance, indicating its superior suitability for extracting tie points from SAR images. 3. When extracting tie points from vertical orbits, the SAR-SIFT algorithm generates a large number of false corners, and its feature descriptors fail to adequately represent terrain features, resulting in higher RMSE values. In contrast, the proposed STSU-SAR-SIFT algorithm eliminates many false corners and provides robust feature descriptions, significantly outperforming the SAR-SIFT algorithm. Due to the different imaging times on vertical orbits, severe image distortions occur, preventing the SIFT, SURF, and AKAZE algorithms from accurately extracting tie points. 4. While extracting dual-matching tie points, although the similarity in same orbit images is high, significant variations in vertical orbit images result in much lower accuracy of tie points on vertical orbits compared to same orbit images. Hence, the accuracy of dual-matching tie points lies between those of the same and vertical orbits. Given that STSU-SAR-SIFT exhibits superior accuracy on both same and vertical orbits compared to SAR-SIFT, it also surpasses SAR-SIFT in accuracy for dual-matching tie points. 5. When extracting triple-matching tie points, STSU-SAR-SIFT continues to perform better than SAR-SIFT, although the difference is not particularly significant.