# 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
## 5 Conclusions

This paper proposes a fast-matching method for optical remote sensing images based on stable and simple feature databases. The advantages of the simple and stable feature database matching method can be summarized as follows. 1. Simplicity: This feature database matching method stores the features extracted from the reference image in the feature database simply and effectively for subsequent matching. Since features do not need to be extracted from the reference data each time matching is performed, this reduces the amount of storage space required for the reference data and speeds up geometric correction and remote sensing image matching. 2. Stability: The feature database extracts features from images of the same region with different time phases and trains stable invariant features (constructs invariant feature point sets) by iterative matching. It increases the correct matching rate by extracting stable feature classes with numerous matches, being adaptable to remote sensing image matching under multiple observation conditions. 3. Scalability: The method in this paper has good reconfigurability because, in addition to the feature algorithms mentioned in the paper, other feature algorithms can be modified by the image resolution of remote sensing images, various geomorphological features of the target area, and various sensor types for acquiring images. The feature database matching technique can be made even more effective in the future by including faster feature algorithms. Instead of repeatedly extracting feature points from the reference image, the fast matching method based on a simple stable feature database can select existing feature points in the corresponding area of the image in the feature database, potentially reducing the storage space of the reference data and improving the efficiency of image processing. Additionally, stable invariant features can be extracted to take part in matching by filtering the stable invariant feature matching parameters to increase the matching accuracy. This matching technique is highly flexible and can be used with many different feature extraction algorithms. **Author Contributions:** Conceptualization, Z.Z. and H.L. ; methodology, Z.Z. and H.L. ; software, Z.Z. and H.L. ; validation, Z.Z. ; formal analysis, Z.Z. ; investigation, Z.Z. ; resources, Z.Z. and H.L. ; data curation, Z.Z. ; writing--original draft preparation, Z.Z. ; writing--review and editing, Z.Z., H.L. and H.Y. ; visualization, Z.Z. ; supervision, Z.Z. ; project administration, H.L. and H.Y. ; funding acquisition, H.L. and H.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences, under Grant ZDBS-LY-JSC036. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** We thank the reviewers for their valuable comments and suggestions. We also would like to thank the production team for revising the format of the manuscript. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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