# 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
#### 3.3.2 Comparison Experiment between Direct Matching and Feature Database Matching

This section contrasts the feature database matching method with the direct application of the reference image matching method to evaluate the proposed method. The comparison evaluation parameters are \(CMR\), \(RMSE\), and \(Time\). \(FMN=6\) is set by the explanation in the previous section. The trained feature database in Section 3.3.1 contains all feature classes with matching numbers. In order to further reduce the storage space and improve the matching efficiency, all feature classes with matching numbers fewer than 6 in the feature database can be deleted. The experiments in this section are conducted on the basis of the deleted feature database. Furthermore, in the block experiment, the block is not directly involved in matching when the number of feature classes obtained from the feature database in the test image block latitude and longitude range is less than 10, which allows for reducing the time required for matching. Only the reference image \(A\) and the feature database were used for comparison in the experiments, and the training image was not used for reference image experiments. This is because the longitude and latitude ranges of the test image and the single training image may overlap, but not necessarily the with exact same scene. However, the feature database contains the range within the whole reference region, and the features within the test image region can be filtered according to the latitude and longitude. If the search ranges of the two methods are not the same for training image matching and feature database matching, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algo-rithm** & **C-1** & **C-2** & **C-3** & **C-4** & **C-5** & **C-6** & **C-7** & **C-8** & **C-9** \\ \hline SIFT & 0.3835 & 0.2424 & 0.4150 & 0.2715 & 0.2144 & 0.1203 & 0.1174 & 0.2299 & 0.2544 \\ \hline SURF & 0.2573 & 0.1032 & 0.1405 & 0.2023 & 0.2872 & 0.1100 & 0.1811 & 0.2214 & 0.2519 \\ \hline KAZE & 0.2178 & 0.1520 & 0.2553 & 0.1853 & 0.2635 & 0.2448 & 0.1415 & 0.3060 & 0.2463 \\ \hline AKAZE & 0.3909 & 0.1901 & 0.1335 & 0.2526 & 0.3090 & 0.1574 & 0.1673 & 0.3305 & 0.2536 \\ \hline ORB & 0.2203 & 0.3554 & 0.1493 & 0.4519 & 0.2214 & 0.1793 & 0.3277 & 0.2868 & 0.3591 \\ \hline FREAK & 0.2366 & 0.1192 & 0.1809 & 0.2683 & 0.2764 & 0.1639 & 0.2306 & 0.3709 & 0.3540 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(RMSE\) (pixel) fluctuations for different feature database matching. Figure 5: \(RMSE\) of different \(FMN\) based on different feature databases. (a) SIFT. (b) SURF. (c) KAZE. (d) AKAZE. (e) ORB. (f) FREAK. they cannot effectively illustrate the effectiveness of the feature database method. Finally, a reference image \(A\) is intercepted based on the latitude and longitude of the test image to create different reference image slices. These reference image slices are used for feature matching with the test images and compared with the feature database. Figure 6 shows that the test image \(C\)-5 matches with the ORB feature database and the reference image, respectively. The left side images of Figure 6a-d are matched with the feature database, and the features from the feature database are displayed on the reference image for a more visual comparison with the right side images. The image on the right side matches the reference image with ORB. Based on the same number of extracted features, it can be seen that the feature database matching method obtains more pairs of matching points and the correspondence of the feature points is more accurate. With direct matching with the reference image using ORB features, the correspondence of the features may be inaccurate, as shown in Figure 6a,b. Although the right side image of Figure 6c,d shows accurate point correspondence, its number of matched point pairs is small, while the feature database matching method obtains a larger number of matched point pairs and is more stable than the right side.