Comparative Analysis of Satellite Images Stitching Methods Based on Local Feature Detection
DOI:
https://doi.org/10.18372/1990-5548.86.20627Keywords:
satellite image stitching, remote sensing, image registration, local feature detection, SIFT, SURF, ORB, BRISK, vector descriptors, binary descriptors, feature matchingAbstract
This paper investigates feature-based methods for satellite image stitching under a unified evaluation framework. Four algorithms – SIFT, SURF, ORB and BRISK - are examined with respect to keypoint detection, descriptor formation, correspondence generation and geometric alignment. A standardized MATLAB workflow is employed: grayscale detection and description, nearest-neighbour matching with a ratio test, robust outlier rejection via RANSAC with model escalation and mask-based blending with content cropping. Approximately fifty image sets spanning diverse landforms are processed; a Sahara Desert example illustrates the protocol. The study’s aim is to characterize the accuracy-efficiency trade-offs of vector (SIFT, SURF) and binary (ORB, BRISK) descriptors in realistic orbital conditions and to provide a transparent basis for method selection in remote-sensing workflows.
References
M. Ivanytskyi, Y. Averyanova, N. Sauliak, and Y. Znakovska, “Machine learning-driven UAV mapping for automated detection of nutritional deficiencies and diseases in wheat,” CEUR Workshop Proceedings, Conference Paper, State University Kyiv Aviation Institute, 2025.
M. Brown and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” International Journal of Computer Vision, vol. 74, no. 1, pp. 59–73, 2007. https://doi.org/10.1007/s11263-006-0002-3
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 60 (2), pp. 91–110, 2004.
https://doi.org/10.1023/B:VISI.0000029664.99615.94
B. Zitová, J. Flusser, “Image registration methods: A survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977–1000, 2003. https://doi.org/10.1016/S0262-8856(03)00137-9
G. Malini and R. Radha, “Comparative analysis of various feature extracting algorithms using satellite images,” Journal of Advanced Research in Dynamical and Control Systems, vol. 12, Issue 7, pp. 545–552, 2020.
https://doi.org/10.5373/JARDCS/V12SP7/20202138
J. Chon, H. Kim, and C.-S. Lin, “Seam-line determination for image mosaicking: A technique minimizing the maximum local mismatch and the global cost,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, pp. 86–92, 2010. https://doi.org/10.1016/j.isprsjprs.2009.09.001
G. Chen, M. Sun, X. Hu, and Z. Zhang, “Optimal seamline detection for orthoimage mosaicking based on DSM and improved JPS algorithm,” Remote Sensing, vol. 10, no. 6, 821, 2018.
https://doi.org/10.3390/rs10060821
L. Li, L. Han, Y. Ye, Y. Xiang, and T. Zhang, “Deep learning in remote sensing image matching: A survey,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 225, pp. 88–112, 2025.
https://doi.org/10.1016/j.isprsjprs.2025.04.001
X. Li, N. Hui, H. Shen, Y. Fu, and L. Zhang, “A robust mosaicking procedure for high spatial resolution remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 109, pp. 108–125, 2015. https://doi.org/10.1016/j.isprsjprs.2015.09.009
X. Yu, J. Pan, S. Chen, and M. Wang, “A flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches,” Information Fusion, vol. 108, Article number 102350, 2024. https://doi.org/10.1016/j.inffus.2024.102350
P. S. Tondewad and M. P. Dale, “Remote Sensing Image Registration Methodology: Review and Discussion,” Procedia Computer Science, vol. 171, pp. 2390–2399, 2020.
https://doi.org/10.1016/j.procs.2020.04.259
X. Zhang, C. Leng, Y. Hong, Z. Pei, I. Cheng, and A. Basu, “Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey,” Remote Sensing, vol. 13, no. 24, Article number 5128, 2021. https://doi.org/10.3390/rs13245128
J. Pan, Q. Zhou, and M. Wang, “Seamline determination based on segmentation for urban image mosaicking,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1335–1339, 2014. https://doi.org/10.1109/LGRS.2013.2293197
A. Riabko, “Methods of satellite images segmentation analysis,” 7th IEEE International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), Kyiv, Ukraine, 2023, pp. 163–167. https://doi.org/10.1109/MSNMC61017.2023.10329167
A. Riabko, “Comparative analysis of SIFT and SURF methods for local feature detection in satellite imagery”, 2024 International Workshop on Computational Methods in Systems Engineering, (CMSE 2024), National Aviation University, Kyiv, Ukraine, vol. 3732, pp. 21–31. ISSN: 16130073.
A. Riabko and V. Hrishnenko “Comparative analysis of BRISK and ORB methods for local feature detection in satellite imagery”, Electronics and Control Systems, National Aviation University, Kyiv, Ukraine, no 1(83), pp. 44–53, 2025. ISSN 1990-5548. https://doi.org/10.18372/1990-5548.83.19879
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
