## 1 Introduction  
Synthetic Aperture Radar (SAR) [1] is a remote sensing technology that can obtain the surface information by transmitting high-frequency electromagnetic waves to the ground and receiving echoes. Different from optical remote sensing, SAR sensor can collect data in any weather conditions, because it can obtain target information by sending electromagnetic waves [2]. SAR image matching is one of the basic and key technologies of SAR image processing. The main task is to carry out geometric correction for two or more images taken at different times, from different sensors or different perspectives, and transform them into the same coordinate system for alignment, so as to carry out subsequent tasks, such as change detection [3], image fusion [4], and agricultural monitoring [5]. Therefore, the research on SAR image matching technology is of great significance for improving the promotion and application of SAR data. The feature point extraction and matching of satellite SAR images mainly completes the feature point extraction of the satellite SAR image to be matched and the registration of the satellite SAR image to be matched with the target satellite SAR image. The key is the matching of the same feature points of two satellite SAR images. The essence of the matching problem of homonymous feature points is to detect \"obvious points\" with stable features in the image [6; 7]. To detect these feature points, three steps are generally required: (1) Select \"points of interest\" with special positions on the image, such as corners, spots, and T-shaped points. The most important feature of interest point detection is repeatability, which requires that the same interest point can be reliably found under different viewing angles. (2) Represented by the neighborhood feature vector of each point of interest, the feature vector descriptor must be unique and robust to noise, geometric deformation, and gray deformation. (3) The feature descriptors between different images can be matched based on the distance between vectors. The dimension of feature vectors will affect the efficiency of matching. Research on SAR image matching methods is currently mainly divided into two major directions [8]: traditional SAR image matching methods and deep learning-based SAR image matching. Among them, traditional SAR image matching methods can be further categorized into methods based on region features (including mutual information, cross-correlation, etc.) [9; 10] and methods based on point features (including Harris, Surf, SIFT, etc.) [11; 12; 13]. Region-based registration methods have the advantage of simplicity and ease of use but face significant challenges in registration accuracy and stability when dealing with large-scale, noisy, or unevenly distributed SAR images. In contrast, feature-based methods improve registration efficiency by extracting prominent features from images [14]. In recent years, significant progress has been made in the computer vision field regarding feature point extraction from multi-scale images and constructing feature vectors within the neighborhood of feature points. SIFT (Scale Invariant Feature Transform) is a typical representative [13]. Introduced by [PERSON] in 1999 at an international computer vision conference, SIFT is a method for describing local features of images based on scale space. It exhibits invariance to image scaling, rotation, and even affine transformations, making it widely applied and effective in optical image matching. In recent years, with the development of deep learning technology, various matching methods based on neural networks have emerged [15]. However, due to the significant challenges in obtaining and generating training samples for SAR images compared to optical images, these methods have not been widely adopted. To enhance the applicability of SIFT to SAR images, subsequent researchers have undertaken a series of improvements to the SIFT algorithm. [PERSON] et al. [16] combined the Best-Bin-First algorithm with SIFT (BBF-SIFT) for SAR image registration.