# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 1 Introduction  
With an increase in satellites and imaging modes that can swiftly and efficiently acquire remote sensing images, the amount of satellite remote sensing image data is exploding. However, the enormous size of the image data has created numerous challenges for traditional remote sensing processing systems, and the processing efficiency of ground-based systems lags behind the satellite observation supply [1]. Although remote sensing processing systems are becoming more intelligent and automated, interactions with satellite systems still take a long time. Traditional image processing systems for remote sensing are hardly capable of processing instantaneous image applications, making it difficult to fully use the extensive remote sensing image resources [2]. The geometric positioning of remote sensing images plays an important role in remote sensing image applications such as image fusion [3; 4; 5], change detection [6; 7; 8], image mosaicing [9; 10; 11], etc. The initial step and the biggest challenge is image matching. Geometrically precise positioning of remote sensing images is highly dependent on effective and quick image matching. The geometric positioning of remote sensing images requires a large amount of ground control information which is the Ground Control Point (GCP) to establish the transformation relationship between images to achieve the consistency of geometric positioning among images. The traditional method is to analyze the selected GCPs in the image manually, which is time-consuming and laborious, and frequently collects GCPs multiple times. The method of establishing a control point database can be used to retrieve control point image slices in the control point database to achieve quick matching of control points [12]. In addition, when geometric correction is performed on future remote sensing images, existing control points can be searched directly from the control point database to match the images, thus enabling the reuse of control points. However, the matching algorithm of the control points and images is challenging. The control points that are easy to recognize by human eyes are not always easy to recognize by computers, and the content of the control points in the control point database needs to be greatly accumulated to ensure matching to remote sensing images of various time phases. With the development of various local invariant feature extraction algorithms, features can be extracted from the reference image as control point information and used to establish the deformation model between images through feature matching to achieve geometric registration for images [13]. The matching efficiency between remote sensing images is increased by the ability to automatically extract control points created by local invariant features without manual acquisition and storage. The development of local invariant feature-matching methods has diversified in recent years, which can mainly be divided into the improvement of local invariant features and the improvement of matching strategies. The main goal of developing feature-matching methods for local invariant features is to reduce the complexity of feature algorithms and increase the robustness and discrimination of feature extraction algorithms, such as the scale-invariant feature transform (SIFT) [14] and its subsequent variants principal components analysis SIFT (PCA-SIFT) [15], gradient location and orientation histogram (GLOH) [16], the speeded up robust feature (SURF) [17], the uniform robust SIFT (UR-SIFT) [18], KAZE [19], accelerated-KAZE (AKAZE) [20], etc. To further increase the effectiveness of the feature matching algorithm, techniques combining accelerated detectors with binary descriptors, such as the oriented FAST and rotated BRIEF (ORB) [21], the binary robust invariant scalable keypoints (BRISK) [22], and the fast retina keypoint (FREAK) [23] algorithms, have emerged.