## 1 Introduction
[22] extended the constrained least-squares (CLS) optimization method developed to register multitemporal SAR images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. In recent years, many deep learning methods have been employed for tie-point extraction in multisource remote sensing imagery. SuperGlue [23] utilizes self-attention and cross-attention mechanisms within graph neural networks to learn features and descriptors. Building on SuperGlue, LightGlue [24] adaptively adjusts the image matching model based on the difficulty of the matches, enhancing efficiency. SODesNet [25] facilitates matching between optical and SAR images using local descriptors. RedFeat [26] employs a mutual weighting strategy to achieve excellent performance in feature matching and image registration tasks. When processing large SAR images, direct feature matching of satellite images leads to a significant number of redundant calculations due to the small overlapping regions of satellite images, resulting in low efficiency in matching image homologous points. To address this challenge, with the rapid development of computers, parallel computing methods have been widely used in image processing, including three main approaches, namely, (1) methods based on the graphics processing unit (GPU) [27], (2) methods based on open multi-processing (OpenMP), and (3) single-instruction multiple data (SIMD)methods. In terms of architecture, GPUs are designed with more logic operations and smaller caches, making them more suitable for handling data-intensive computational tasks, while CPUs are better suited for processing logic control-intensive tasks, with fewer logic operations and larger caches. Currently, many scholars have improved the SIFT algorithm using GPUs and multi-core CPUs. [PERSON] et al. [28] put forward a parallel SIFT approach to symmetric multiprocessor (SMP) systems, which mainly uses multi-core CPUs to accelerate the construction of feature point detection, descriptor construction, and Gaussian scale space parts of the SIFT algorithm. [PERSON] et al. [29] put forward an approach that relied on Open Computing Language (OpenCL) to enhance the performance of the SIFT approach. [PERSON] et al. [30] improved CUDA-SIFT to enable it to handle larger image blocks and better allocate video memory space. The algorithms above mainly use memory storage techniques to enhance the computational efficiency of the SIFT approach and only use a single method to accelerate the SIFT algorithm, without considering the different advantages of GPUs, OpenMP, and CPUs and how to combine them for further acceleration. In summary, although matching approaches for SAR images have achieved significant advancements, further research is needed to solve such problems as a multitude of incorrect feature points resulting from speckle noise, the robustness of feature descriptors, and image matching efficiency. The proposed methodology in this article offers a robust way to match features, STSU-SAR-SIFT, which is suitable for multi-scene SAR image data, significantly improving both matching accuracy and efficiency. The noteworthy contributions of this study are:  
1. A method is put forward to obtain high-precision feature points that are not affected by threshold values by combining current scale information. 2. A more robust feature descriptor is constructed using the Gradient Location and Orientation Histogram (GLOH) method with multiple scales and directions. 3. An acceleration algorithm based on both CPU and GPU is proposed to develop the matching efficiency of large-area images. The remaining sections of this paper are organized as follows: Section 2 begins with a brief introduction to the improved method of SAR-SIFT, followed by a description of the modifications proposed in this study. Section 3 reports the experimental results of different methods on various datasets. Section 4 presents the conclusions drawn from the study.