# 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
As a result, the set \(\mathit{FMN}\) is only added to the statistics when the number of feature classes extracted from the feature database in accordance with \(\mathit{FMN}\) is greater than or equal to 50. As can be seen in Figure 4, the difference in the stability of the feature algorithm demonstrated in the feature database matching process is what causes the difference in the horizontal coordinates of experiments. Some feature algorithms, such as KAZE and AKAZE, are stable in extraction and always match, and the number of feature classes with a high number of matches is higher. However, when ORB and FREAK use binary descriptor matching, which is less stable than the gradient descriptor, the number of feature classes with a high number of matches is lower, and its maximum \(\mathit{FMN}\) value is smaller. Figure 4 shows the correct feature matching rate of each image in the test set \(C\) and the feature databases based on different feature algorithms. From the figure, it can be seen that as \(\mathit{FMN}\) gradually increases, the feature correct matching rate of the test image basically shows an increasing trend, and the increasing trend gradually becomes slower. This indicates that features with more matches in the feature database are more stable in the image of that region, and filter features, which always appear in that experimental region, can always match with the features extracted from the test images. Although the larger the \(FMN\), the more stable the feature classes extracted from the feature database, it is not true that the higher the value of \(FMN\) the better the test image matches the feature database. It can also be observed from Figure 4 that the growth rate of \(CMR\) for matching the feature database to the test images is not obvious after \(FMN=6\). Especially for the AKAZE feature database, there is a significant decrease in \(CMR\) in the matching of test images \(C\)-1, \(C\)-6, because the larger the \(FMN\), the smaller the number of extracted feature classes. The limited number of feature classes can also not reach a particularly high \(CMR\). Therefore, in the comparison experiment with the reference image, we generally set \(FMN=6\), which has both a high \(CMR\) and a certain number of feature classes. Figure 5 shows \(RMSE\) values after matching the test images with the feature databases based on different feature algorithms according to different \(FMN\) values. As can be seen from the figure, as \(FMN\) gradually increases, the \(RMSE\) obtained by the feature database matching methods based on different feature algorithms stays basically the same. As shown in Table 4, the \(RMSE\) fluctuations of the feature database matching results based on various feature operators are fewer than 0.5 pixels under various test images. This shows that \(FMN\) has little influence on the matching results and also little effect on the precision of geometric registration following a match. Combined with the above description, it can be concluded that with the gradual increase of \(FMN\), the overall \(CMR\) based on the stable feature database matching method shows an increasing trend, while \(RMSE\) is not affected by the change in the number of filtering matches. This indicates the feasibility and effectiveness of filtering stable feature matching strategies based on the stable feature database matching method. Moreover, when using the feature database matching method to correct remote sensing images, we can typically set \(FMN=6\) to filter feature classes for matching to ensure that the number of features extracted from the feature database after filtering according to \(FMN\) is sufficient and the number of correct matches is certain. Figure 4: \(CMR\) of different \(FMN\) based on different feature database. (**a**) SIFT. (**b**) SURF. (**c**) KAZE. (**d**) AKAZE. (**e**) ORB. (**f**) FREAK.