# 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
implementation algorithms such as ORB or FREAK may be selected for the construction of feature databases. If the tendency is to have good matching stability and a large number of stable feature classes, the feature database can be constructed by choosing algorithms such as KAZE or AKAZE. ## 4 Discussion

The experiments in Section 3 demonstrate the usefulness and stability of the feature database matching approach using data set \(D\) and different feature extraction methods. The feature database matching method based on different feature algorithms can achieve 100% stable matching of the test image on the test set \(C\), but the direct matching of the test image with the reference image may not match successfully. When both matching methods are successful, the feature database matching approach has a higher rate (mostly above 30%) of correctly matching feature points than direct matching. This is because the simple and stable feature database matching method takes into account the differences in feature descriptors under multiple observation conditions. The proposed method uses an iterative matching method to train stable feature points and aggregate multiple descriptors at the same feature point location, which greatly increases the probability of successful matching with the test image. Moreover, the results from the feature database matching approach are not significant deviations from the direct matching results in terms of \(RMSE\). Additionally, compared with direct matching, the feature database matching method saves a large amount of time because it skips the extraction of features from the reference image. For most feature algorithms, the matching of the feature database has been shown to take less time than direct matching by more than 40%. However, there are still some parts to be improved in this paper. First, according to the iterative matching strategy of the feature database in this paper, it is theoretically possible to automatically update the feature database while continuously inputting the test

Figure 10: Different feature database comparison (**a**) \(CMR\). (**b**) the number of feature classes. (**c**) the number of matches. (**d**) match time. image. However, training and testing are separated in the paper, and there is no mention that the test image can also update the feature database. This is because the lack of coding ability makes feature clustering take some time, and the matching process plus the feature database update process will become less efficient. Second, the time to extract the features from the feature database is almost negligible compared with the time to extract the features from the reference image. However, the experimental results show that inherently efficient operators such as SURF do not save as much time as other operators using the feature database method; because the descriptors of SURF are floating points, it takes more time to read them by the program written by itself. This can be solved by improving the database reading procedure. Third, the current experimental method has been verified only for optical remote sensing images, while this framework is theoretically applicable to a wide range of sensors. In the future, we hope to optimize the program so that we can also train based on the test image when the test image is matched and perform fast updates to the feature database. Furthermore, we will build the feature database using more stable feature algorithms such as histogram of orientated phase congruency (HOPC) [36] and radiation-variation insensitive feature transform (RIFT) [37], among others, that are suitable for multi-modal image matching to experiment on multi-modal images. Based on the iterative approach of the feature database, the invariant features within the scene are found by the input multi-modal images, and the descriptors of multiple styles under the same location are clustered to make the feature database approach more universal.