## 4 Conclusions  
This study employs the Shi-Tomasi corner detector for feature point detection, thereby circumventing the issue of inconsistent quantity and quality of initially screened feature points due to manually set empirical thresholds. Subsequently, the SUSAN operator is utilized to eliminate erroneous feature points affected by multiplicative noise, reducing the impact of noise on the selected feature points in SAR imagery. Finally, a feature descriptor is constructed using the Laplacian operator and multi-scale feature point detection methods, which retains multi-scale information and enhances the distinctiveness of the descriptor. Experimental results on GF-3 and Sentinel-1 data demonstrate that the STSU-SAR-SIFT algorithm has removed a majority of erroneous feature points, improving the robustness and registration accuracy of the feature points. The matching point accuracy of this algorithm has been enhanced by approximately 0.5-2 pixels compared to other algorithms. The Sentinel-1 imagery selected for this study includes portions of coastal areas, where the marine representation in the SAR images appears as a large dark region. The features within this area are highly similar and are significantly affected by noise. Consequently, without proper processing, these areas typically degrade the accuracy of feature matching. In this paper, we employed the SUSAN operator to eliminate noise and construct a multi-scale, eight-directional feature descriptor, thereby mitigating the impact of the aforementioned dark marine areas. As a result, the matching accuracy of coastal areas that include oceans approaches that of inland regions. Additionally, the algorithm's efficiency has been increased by about three times through the use of GPU acceleration. Although the algorithm in this paper improves the matching accuracy and efficiency to a certain extent, there is still room for further improvement in the extraction of triple feature points, and it can be further split in the running efficiency to make full use of threads.  
Conceptualization, [PERSON] and W.W.; methodology, [PERSON], J.C. and Z.H.; software, J.C.; validation, [PERSON] and J.C.; formal analysis, [PERSON], J.C. and Z.H.; investigation, [PERSON] and J.C.; resources, Z.H.; data curation, [PERSON] and J.C.; writing--original draft preparation, W.W., J.C. and Z.H.; writing--review and editing, [PERSON], J.C. and Z.H.; visualization, [PERSON] and J.C.; supervision, Z.H.; project administration, Z.H.; funding acquisition, [PERSON] and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Yangtze River Delta Science and Technology Innovation Community Joint Research (Basic Research) Project (2024 CSJZN01303 and 2024 CSJZN01304).  
The data that support the findings of this study are available in the Copernicus Data Space Ecosystem at [[https://dataspace.copernicus.eu/data-collections/sentinel-data/sentinel-1](https://dataspace.copernicus.eu/data-collections/sentinel-data/sentinel-1)]([https://dataspace.copernicus.eu/data-collections/sentinel-data/sentinel-1](https://dataspace.copernicus.eu/data-collections/sentinel-data/sentinel-1)) (accessed on 1 July 2025).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **CPU (Hours)** & **Multi-Thread CPU (Hours)** & **CPU and GPU (Hours)** \\ \hline Sentinel-1 & 2.7 & 1.3 & 0.75 \\ GF-3 & 8.3 & 5 & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time-consuming comparison of different data sets.  
**Acknowledgments:** The authors sincerely appreciate the anonymous reviewers for their insightful comments and suggestions, which have significantly contributed to enhancing the quality of this paper.