# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 2 Materials and Methods
### The Building Method of Simple Stable Feature Database
#### 2.2.2 Iterative Matching Strategy for Feature Database Methods
The feature databases constructed by each operator perform well in matching comparison experiments, and it can be seen that the feature database matching algorithm is universal for a variety of feature algorithms. This subsection performs matching experiments based on test image C-9 to compare the performance of feature databases based on different feature algorithms. Figure 10 compares the results of the test image C-9 matching with the feature database based on different feature methods. From Figure 10a, it is clear that the matching rate increases gradually as the value of \(FMN\) increases and that the upward trend progressively slows down. The upward trend of the correct matching rate of each feature database in this figure is roughly the same. At an \(FMN\geqslant 9\), the \(CMR\) of the test image that matches the ORB feature database features decreases, probably due to the insufficient number of stable filtered feature classes. From Figure 10b,c, it can be seen that the number of feature classes extracted from the feature databases and the number of successful matches with the test image gradually decrease with the gradual increase of \(FMN\), and the trend is gradually slowing. At different \(FMN\) values, the number of stable feature classes filtered from the KAZE, AKAZE, SIFT, and SURF feature databases is relatively high and the number of feature classes obtained from the ORB and FREAK feature databases is low. The totality of the \"blob\" detector is generally better than the \"corner\" point detector [35], so the \"blob\" detector can retain more stable invariant feature classes in the iterative matching process of the feature database. From Figure 10d, we can see that the KAZE operator takes the longest time among the feature database matching methods. Due to the complexity of the KAZE operator, it takes a long time even if the matching time is approximately half as long using the feature database method matching. It is observed that all curves in the figure show a slight downward trend because the number of filtered feature classes gradually decreases with increasing \(FMN\) values and the time to read the features from the feature database also decreases. Furthermore, in the blocking experiment, when the number of feature classes obtained from the feature database in the test image blocking latitude and longitude range is less than 10, the block is not directly involved in matching, which also causes a decrease in time. In combination with the above analysis, the performance comparison between feature databases is mainly determined by the use of feature algorithms within the feature database. In the proposed feature database matching method, KAZE is relatively stable, but it takes a long time. When compared to KAZE, AKAZE requires much less time and has fewer stable features, but the construction of the nonlinear scale space still takes a certain amount of time. SIFT and SURF are not as stable as KAZE and AKAZE and are not as fast as ORB and FREAK. ORB and FREAK are the most efficient, but the combination of corner detectors and binary descriptors is not as stable as blob detectors and floating-point descriptors. We can construct simple and stable feature databases according to different application requirements. If the tendency is for fast matching and high matching efficiency, automatic \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Stable Direct** & **Stable Feature** & **Average CMR** & **RMSE Absolute** & **Average Time** \\
**Match/Image** & **Database** & **Increase/\%** & **Difference Mean/pixel** & **Reduction/\%** \\ \hline SIFT & 9 & 9 & 42.23 & 0.1251 & 51.31 \\ \hline SURF & 8 & 9 & 40.38 & 0.101 & 36.83 \\ \hline KAZE & 9 & 9 & 32.78 & 0.064 & 45.66 \\ \hline AKAZE & 9 & 9 & 34.54 & 0.0803 & 43.08 \\ \hline ORB & 5 & 9 & 28.76 & 0.1508 & 40.33 \\ \hline FREAK & 8 & 9 & 33.61 & 0.1685 & 48.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of parameters between direct matching method and feature database matching method.