# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
## 3 Results
### Position Error Experiment and Attitude Error Experiment
As seen in the quantitative comparison results above, the SIFT algorithm extracted an average of only about 8 inliers, demonstrating that the algorithm is not sufficiently robust when applied to the interference fringe position error dataset. Although the SURF algorithm extracted more inliers, the values for its three indicators of recall and F1-Score were the worst of the three algorithms, indicating that, although the SURF algorithm extracted more points, its matching success rate is poor and its overall performance was not good. In comparison, the average number of inliers extracted by the proposed algorithm reached about 72, indicating that the algorithm is highly robust and can adapt to the needs of aircraft IMU for matching algorithms in a position error environment. The average precision of this algorithm is 0.103, which is higher than that of the two other algorithms, indicating that the branch points extracted by the algorithm are more valuable than those extracted using the two other algorithms. The average recall of this algorithm is 0.128, which is higher than that of the remaining two algorithms. As seen in Figure 18b, although the recall of SIFT and SURF is good in several groups of experiments, it again proves the poor stability of SIFT and SURF due to large up-and-down fluctuations. As seen in Figure 18d as well as Table 3, the comprehensive performance of this algorithm is better than that of the two other algorithms. Second is the attitude error experiment. Figure 19 shows the matching effect of the three algorithms when the simulated interference fringes have a thirty degree yaw angle error. Figure 19a-c show the SIFT algorithm, SURF algorithm, and the algorithm proposed in this paper, respectively, and outlier points were eliminated in all three algorithms. The  
Figure 18: Quantitative comparison of interference fringe-matching algorithms in the position error experiment. (**a**) Precision. (**b**) Recall. (**c**) Inliers. (**d**) F1-Score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **Inliers** & **F1-Score** \\ \hline SIFT & 0.018 & 0.019 & 7.878 & 0.016 \\ SURF & 0.019 & 0.016 & 15.780 & 0.012 \\ Proposed & 0.103 & 0.128 & 72.024 & 0.144 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average of the quantitative evaluation indicators in the position error experiment. number of correct matching points obtained by the proposed algorithm is still the largest when the interference fringes are rotated and shifted. To quantitatively describe the algorithm performance, the above algorithm is run on the dataset of 61 simulated interference fringes with a yaw angle error of plus or minus 30\({}^{\circ}\) and an error interval of 1\({}^{\circ}\). Inliers, precision, recall, and F1-Score were quantified, and the results are shown in Figure 20. The mean values of the quantitative evaluation metrics are shown in Table 4.  
Figure 19: Comparison of interference fringe-matching algorithm results in the attitude error experiment. (**a**) SIFT. (**b**) SURF. (**c**) Proposed algorithm. Figure 20: Quantitative comparison of interference fringe-matching algorithms in the attitude error experiment. (**a**) Precision. (**b**) Recall. (**c**) Inliers. (**d**) F1-Score. From the above quantitative comparison results, it can be seen that the average number of inliers extracted by the SIFT algorithm is 8, which demonstrates that the SIFT algorithm is still not robust when applied to the attitude error dataset. Although the SURF algorithm extracts many inliers, its precision, recall and F1-Score are the worst among the three algorithms. In contrast, the average number of inliers extracted by this algorithm is about 62, the average precision is 0.093, the average recall is 0.112, and the F1-Score is 0.108, which is higher than the other two algorithms. This proves that the algorithm is effective and can meet the requirements of the aircraft IMU for the matching algorithm in the attitude error environment.