## 1 Introduction  
Image matching is fundamental to methods such as image detection, image stitching, image fusion, and 3D reconstruction [1, 2, 3, 4], with the final accuracy of these methods directly influenced by the number and precision of keypoints. Currently, SAR image matching faces two major challenges: firstly, the ubiquitous speckle noise in SAR images severely degrades the precision of keypoint extraction; secondly, the immense size of these images results in extremely low efficiency of direct matching. At present, two main strategies can be employed for SAR image matching: area-based and feature-based methods [5, 6, 7]. Area-based methods register images via the similarity criteria, commonly used ones of which include cross-correlation [8] and mutual information [9], as well as a series of improved methods based on these criteria [10]. Area-based methods mostly estimate matching parameters by computing the information entropy of both the reference and target images, using mutual information between the two images as a similarity criterion, and maximizing the mutual information to convert the matching problem into an optimization problem. However, the primary limitation of this method is that optimization search consumes a lot of time, leading to low matching efficiency. In contrast, feature-based methods extract characteristics from images and determine matching points among these characteristics. Extracting features based on lines and surfaces is a challenging task, and some of these features may not even be present in remote-sensing images [11]. Commonly used features include points, lines, and areas, with point-based feature methods of high accuracy and effectiveness [12], being more widely used in image matching, such as the Scale-invariant feature transform (SIFT) [13] and speeding up robust feature (SURF) [14] algorithms. However, since algorithms like SIFT and SURF amplify speckle noise in SAR images during the establishment of Gaussian difference pyramids, they tend to produce many false feature points during the feature extraction process. To address this issue, numerous scholars have put forward a series of improved algorithms based on the SIFT algorithm [15]. [PERSON] et al. [16] proposed the adaptive control of feature points determined by the quantity of extremum points, mean contrast, and mean entropy within each scale to achieve uniform distribution of image feature points. However, this approach is only suitable for optical images. [PERSON] et al. [17] replaced Gaussian filtering with bilateral filtering and enhanced matching accuracy through a bidirectional matching strategy, but reduced the operational efficiency. In order to preserve more image details and reduce the loss of details, [PERSON] et al. [18] employed a non-linear filter to construct the scale space. However, this approach reduces the scale invariance. [PERSON] et al. [19] put forward the SAR-SIFT algorithm, which calculates the gradient of the SAR image using the Ratio of Exponentially Weighted Averages (ROEWA) operator and takes the logarithm of the gradient to get rid of the impact of speckle noise. [PERSON] et al. [20] proposed the ROEWA-Blocks approach by introducing the ROEWA operator in the SAR-SIFT algorithm. The ROEWA-Blocks algorithm has been used to address the matching problem of the region that may be suddenly darkened in SAR images. However, in these algorithms, speckle noise greatly affects SAR images, making it difficult to directly identify topography. Single-scale feature descriptors are not sufficient to solve the topographic similarity problem caused by speckle noise. The matching accuracy will be low when the number of feature descriptors and feature directions is small. [PERSON] et al. [21] proposed a registration algorithm to compensate for the possible inaccuracy of the trajectory sensor during the synthetic aperture radar (SAR) image acquisition process. [PERSON] et al.