# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 1 Introduction
These techniques are not only quick at matching but also have exceptionally low storage space requirements, which helps resolve the issue with real-time matching methods. For matching strategies, feature matching techniques are improved, and various techniques are used to increase the accuracy of feature extraction on remote sensing images. For example, [PERSON] et al. proposed a novel remote sensing image feature matching error removal method, introducing a guided matching strategy to significantly improve the number of true matches without sacrificing accuracy [24]. [PERSON] et al. proposed the adaptive regional multiple feature matching method (ARMF), which introduces an adaptive region search strategy to adaptively select matching feature regions using pyramidal scaling techniques to extract multiple types of features and adaptively select appropriate feature descriptors [25]. [PERSON] et al. proposed a closed-feedback SIFT system based on SIFT, containing a correction return loop to improve the position accuracy by replacing the current sensed image via an iterative approach [26]. The key to the above-mentioned local invariant feature-based matching approaches lies in pairing highly-repeatable common features between the same scene images under multiple observation conditions [27]. These features are invariant to different images of the same scene, should be unique within the same image, and can be distinguished from other feature points within the image using descriptors [28]. However, many existing local invariant feature-matching methods have problems in the following aspects. Firstly, it is difficult for existing algorithms to ensure that the extracted local invariant features remain invariant in multi-temporal, multi-radiation, and multi-view remote sensing image data. Because many feature extraction methods are based on grayscale images or gradient information to detect keypoints that are easily affected by noise and illumination, leading to wrong detection and omission, and the descriptors cannot express features correctly, under different observation settings, there will not be enough feature point correspondences to guarantee the accuracy of image matching [29]. Secondly, these feature-matching methods require accurate geographic information from reference images. The acquisition and selection of reference images restrict remote sensing image matching under multiple observation conditions, since it is challenging to guarantee effective matching with a single reference image, which is less flexible. Additionally, with the rapid development of remote sensing imaging technology, the resolution and size of the collected multi-observation condition images have been greatly increased, and the storage space occupied has increased. This makes reading remote sensing images and extracting features from remote sensing images less efficient. In response to the above problems, this paper proposes a fast-matching method for optical remote sensing images based on simple and stable feature database matching. First, considering that the feature-based matching method is constrained by the selection of the reference image, the feature database is applied to the remote sensing image matching method. The feature database refers to the control point database production method and stores the local invariant features in a simple and effective form. These features can be directly applied in the subsequent geometric positioning, saving the time of extracting features in the reference image and improving the image matching efficiency. Secondly, this paper combines the training-feedback mechanism to iteratively match the feature database, construct stable feature sets, and cluster the descriptors of stable feature points under multi-observation conditions. After the feature database is trained, multiple relatively stable feature point sets are stored in the database. Finally, the test images are matched with the feature database to achieve fast and accurate image geometry correction.