# 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
Figure 6: Comparison of the reference image matching method and the feature database matching method based on \(C\)-5. (**–d**) \(C\)-5 image block matching. The left image is the result of matching with the feature database, and the right image is the result of matching with the reference image. The left end of each image connecting line is the reference image, and the right end is the test image. The comparison of the \(CMR\) between the direct matching method with the reference image and the feature database matching methods based on different feature algorithms is shown in Figure 7. In the experiment, if the test image blocks and the reference image slice blocks or feature database matching point pairs exceed 10, the matching is considered stable, and anything less than this threshold is considered unstable. It is observed that there are cases where \(CMR\) is 0 in the figure, such as \(C\)-7 in Figure 7b, \(C\)-5 in Figure 7e, etc., because in the reference image slices and the test image in the block matching process, the number of matches obtained by none of the blocks exceeds the set threshold, and we consider the matching to fail at this time. It is obvious that directly matching with the reference image using classical operators does not match all test images successfully, but by using the simple stable feature database to match, every test image in the test set \(C\) can be correctly matched. This is because our proposed feature database matching method, which filters out stable feature classes and assembles descriptors of stable features under multiple observation conditions, has a higher possibility of successful matching with different optical satellite images. Therefore, in the comparison experiments, the feature database can achieve stable matching with all test images in the test set. Furthermore, in the case of both methods, the feature database matching method based on different feature operators has essentially an over 30% greater correct feature point matching rate than direct matching using this feature operator, as shown in Table 5.

Figure 8 shows the \(RMSE\) comparison between the direct matching methods and feature database matching methods based on different feature algorithms. The \(RMSE\) of the feature database matching approach based on different feature operators is approximately equivalent to that of the direct matching with the reference image, with an absolute difference average value of fewer than 0.3 pixels. The \(RMSE\) of the matching instability case will not be shown in the graph. As can be seen, geometric registration on large-scale remote sensing images can be performed with a certain degree of accuracy using the feature database matching method. For more details, see Table 5.

Figure 7: \(CMR\) comparison of the direct matching method and the feature database matching method. (a) SIFT. (b) SURF. (c) KAZE. (d) AKAZE. (e) ORB. (f) FREAK. Figure 9 compares the matching time for direct matching and feature database matching methods based on different feature algorithms. The figure clearly shows that the feature database matching method always takes less time than the direct matching method using the reference image slice, because the feature database matching method eliminates the step of extracting features from the reference image. Table 5 shows that, compared to direct matching, the feature database matching method based on various feature operators typically reduces the time taken by more than 40%. Figure 8: \(RMSE\) comparison of direct matching method and feature database matching method. (a) SIFT. (b) SURF. (c) KAZE. (d) AKAZE. (e) ORB. (f) FREAK. Figure 9: Match time comparison of direct matching method and feature database matching method. (a) SIFT. (b) SURF. (c) KAZE. (d) AKAZE. (e) ORB. (f) FREAK. #### 3.3.3 Difference between Feature Database

Six classical feature algorithms--SIFT, SURF, KAZE, AKAZE, ORB, and FREAK--are selected to construct simple and stable feature databases in the above experiments.