UDC 528.7
This work is aimed at investigating the effectiveness of the adapted AKAZE algorithm for processing images of the surface of glaciers, which have a complex structure due to the variety of textures, cracks and variability of lighting conditions. Glacial surfaces pose a challenge to traditional image processing methods, as the qualities of the connecting points can vary significantly depending on the environment. In the study, preliminary image processing was carried out, which made it possible toincrease the accuracy offinding the connecting points necessary for accurate modeling. Using the Python programming language, the AKAZE algorithm was implemented to find and measure connecting points in images. The images were then transferred to the PHOTOMOD 7 software package, where several subsequent processing steps were carried out to create a digital relief model. Afull cycle of processing ofthe same block was also performed using internal algorithms in the PHOTOMOD 7 and Agisoft Metashape software package to compare results and evaluate quality. A comparative analysis of the obtained elevation matrices demonstrated the effectiveness of the AKAZE algorithm in the context of processing complex glacial surfaces. The results of the study highlight its potential in geoinformation tasks and open up new horizons for further research in the field of photogrammetry and monitoring of natural objects.
unmanned aerial photography, aerial photographs of the glacier, connecting points, detector, descriptor, phototriangulation, digital terrain model
1. Kääb A., Huggel C., Fischer L., et al. Remote sensing of glacier- and permafrost-related hazards in high mountains: an overview // Natural Hazards and Earth System Science. 2005. Vol. 5(4). P. 527–554. DOIhttps://doi.org/10.5194/nhess-5-527-2005. https://www.researchgate.net/publication/29629031_Remote_sensing_of_glacier-_and_permafrost-related_hazards_in_high_mountains_An_overview
2. Paul F., Bolch T., Kääb A., et al. The glaciers climate change initiative: Methods for creating glacier area, elevation change and velocity products // Remote Sensing of Environment. 2015. Vol. 162. P. 408–426. DOIhttps://doi.org/10.1016/j.rse.2013.07.043. https://doi.org/10.1016/j.rse.2013.07.043
3. Bhardwaj A., Sam L., Akanksha Martín-Torres F.J., et al. UAVs as a remote sensing platform in glaciology: present applications and future prospects // Remote Sensing of Environment. 2016. Vol. 175. P. 196–204. DOIhttps://doi.org/10.1016/j.rse.2015.12.029. https://doi.org/10.1016/j.rse.2015.12.029
4. Skrypicyna T.N., Zaharov V.G., Kiseleva A.S. i dr. Evolyuciya rel'efa vyvodnogo lednika Dolk (zaliv Pryuds, Vostochnaya Antarktida) po dannym bespilotnyh aerofotos'emok 2017–2019 godov // Izvestiya vuzov «Geodeziya i aerofotos'emka». 2021. T. 65, № 5. S. 517–528. DOIhttps://doi.org/10.30533/0536-101X-2021-65-5-517-528. https://www.elibrary.ru/download/elibrary_47398893_14970986.pdf
5. Kazakov E.E., Volkov V.A., Demchev D.M. Sistema operativnogo monitoringa morskogo l'da v Arktike, osnovannaya na otkrytyh sputnikovyh radiolokacionnyh dannyh // Sbornik materialov II Mezhdunarodnoy nauchno-prakticheskoy konferencii «Geodeziya, kartografiya, geoinformatika i kadastry. Ot idei do vnedreniya». SPb.: Politehnika, 2017. S. 33–39.
6. Nefedova O.A., Chibunichev A.G., Zhurkin I.G. Issledovanie algoritmov avtomaticheskogo nahozhdeniya i izmereniya svyazuyuschih tochek na snimkah lednika, poluchennyh s BVS // Izvestiya vuzov «Geodeziya i aerofotos'emka». 2024. T. 68, № 5. S. 31–43. DOIhttps://doi.org/10.30533/GiA-2024-037. https://miigaik.ru/journal/archive/2024/2024_68_5_RU/GiA-2024-037.pdf
7. Tareen S.A.K., Saleem Z. A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK // International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). Sukkur: IEEE, 2018. P. 1–10. DOIhttps://doi.org/10.1109/ICOMET.2018.8346440. https://ieeexplore.ieee.org/document/8346440
8. Yang X., Cheng K.T. LDB: An ultra-fast feature for scalable augmented reality // IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Atlanta: IEEE, 2012. P. 49–57. DOIhttps://doi.org/10.1109/ISMAR.2012.6402537. https://ieeexplore.ieee.org/document/6402537
9. Kalms L., Khaled M., Gzhringer D. Accelerated Embedded AKAZE Feature Detection Algorithm on FPGA // HEART’17: Proceedings of the 8th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies. New York: ACM, 2017. P. 10. DOIhttps://doi.org/10.1145/3120895.3120898. https://doi.org/10.1145/3120895.3120898
10. Smirnov A.V., Skrypicyna T.N., Zubkov S.A. Osobennosti fotogrammetricheskoy obrabotki s'emki aysbergov, poluchennoy s bespilotnogo vozdushnogo sudna // Izvestiya vuzov «Geodeziya i aerofotos'emka». 2022. T. 66, № 3. S. 42–59. DOIhttps://doi.org/10.30533/0536-101X-2022-66-3-42-59. https://www.elibrary.ru/download/elibrary_50457504_97945229.pdf
11. James M.R., Antoniazza G., Robson S., et al. Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off‐nadir imagery // Earth Surface Processes and Landforms. 2020. Vol. 45. Is. 10. P. 2251–2271. DOIhttps://doi.org/10.1002/esp.4878. https://doi.org/10.1002/esp.4878
