MAPPING MINE RISK USING LARGE-SCALE MULTISPECTRAL IMAGERY
DOI:
https://doi.org/10.17721/1728-2713.108.14Keywords:
humanitarian demining, UAVs, remote sensing, vegetation indices, decoding, automation, mappingAbstract
Background. The full-scale armed aggression against Ukraine has resulted in significant changes in natural and anthropogenic landscapes. This negatively affects the safety of the population and the development of the country's economy. Mines, unexploded ordnance and other explosive objects pose an extraordinary threat. To identify mines, we analyzed the possibility of using spectral images obtained from UAVs for mapping.
Methods. For the mapping tasks, high-resolution images of test images of the test site located in Khmelnytsky region (Ukraine) with dummy mines placed on the surface and at a shallow depth were used. Data processing was performed in ArcGIS PRO, using Python programming.
Results. The article compares the methods of remote mine identification by different sensors according to the literature. A methodology for decoding high spatial resolution geo-images obtained from UAVs in the visible and thermal ranges of the spectrum to detect mines in open areas for humanitarian demining was tested. The image processing algorithm for mine detection, analysis and interpretation of the results using the Python language was further developed.
Conclusions. The comparative analysis and experimental work emphasize the effectiveness of using multispectral images, in particular, the Soil Water Index (SWI) calculated on their basis, for mine identification on multispectral images obtained from UAVs. The use of SWI provides an accuracy of mine identification of only about 70% and can be used only for preliminary assessment of the contamination of territories by explosive objects.
References
Alqudsi, Y. S., Alsharafi, A. S., & Mohamed, A. (2021, July). A review of airborne landmine detection technologies: Unmanned aerial vehicle-based approach. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1–5). IEEE.
Baghbadorani, A. A., Holbrook, S., Martin, E. R., Ripepi, N. S., & Libbey, B. (2022). Radar Imaging Applications for Mining and Landmine Detection.
Baur, J., Steinberg, G., Nikulin, A., Chiu, K., & de Smet, T. S. (2020). Applying deep learning to automate UAV-based detection of scatterable landmines. Remote Sensing, 12(5), 859.
Bonchkovskyi, O., Ostapenko, P., Shvaiko, V., & Bonchkovskyi, A. (2023, November). A Comprehensive Methodological Approach to Assessing War-Induced Soil Disturbances in the Kharkiv Region, Ukraine. In 17th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment (Vol. 2023, No. 1, pp. 1–5). European Association of Geoscientists & Engineers.
Cabinet of Ministers of Ukraine. (2024, June 28). On approval of the National Mine Action Strategy for the period until 2033 and approval of the operational plan of measures for its implementation in 2024–2026. Order No. 616-р. https://zakon.rada.gov.ua/laws/show/616-2024-р#Text
Deans, J., Gerhard, J., & Carter, L. J. (2006). Analysis of a thermal imaging method for landmine detection, using infrared heating of the sand surface. Infrared Physics & Technology, 48(3), 202–216. https://doi.org/10.1016/j.infrared.2005.06.003
Dovbnia, M. (2024). Comparative analysis of drones for demining Ukrainian territories [in Ukrainian].
Dovhii, S., Lyalko, V., Babiychuk, S., Kuchma, T., Tomchenko, O., & Yurkiv, L. (2019). Fundamentals of remote sensing of the Earth: history and practical application [in Ukrainian].
Elbakary, M. I., & Alam, M. S. (2008, March). Mine detection in multispectral imagery data using constrained energy minimization. In Optical Pattern Recognition XIX (Vol. 6977, pp. 63–71). SPIE.
Fernández, M. G., López, Y. Á., Arboleya, A. A., Valdés, B. G., Vaqueiro, Y. R., Andrés, F. L. H., & García, A. P. (2018). Synthetic aperture radar imaging system for landmine detection using a ground penetrating radar on board a unmanned aerial vehicle. IEEE Access, 6, 45100–45112.
Horbulin, V. P. (2022). The global problem of demining: the Ukrainian vector. Bulletin of the National Academy of Sciences of Ukraine, 2, 3–13 [in Ukrainian].
Hupy, J. (2008). The environmental footprint of war. Environment and History, 14(3), 405–421.
Kasban, H., Zahran, O., Elaraby, S. M., El-Kordy, M., & Abd El-Samie, F. E. (2010). A comparative study of landmine detection techniques. Sensing and Imaging: An International Journal, 11, 89–112.
Lamorski, K., Pręgowski, P., Swiderski, W., Szabra, D., Walczak, R. T., & Usowicz, B. (2002). Thermal signatures of land mines buried in mineral and organic soils–modelling and experiments. Infrared Physics & Technology, 43(3–5), 303–309.
Miller, T. W., Borchers, B., Hendrickx, J. M., Hong, S., Dekker, L. W., & Ritsema, C. J. (2002, April). Effects of soil physical properties on GPR for landmine detection. In Fifth International Symposium on Technology and the Mine Problem (pp. 1–10).
Popov, M. O., Stankevich, S. A., Moso, S. P., Titarenko, O. V., Dugin, S. S., Golubov, S. I., & Andreiev, A. A. (2022). Method for Minefields Mapping by Imagery from Unmanned Aerial Vehicle. Advances in Military Technology, 17(2), 211–229 [in Ukrainian].
Stetsiuk, V., & Kovalchuk, I. (2019). Beligerative properties of the relief. Bulletin of the Taras Shevchenko National University of Kyiv. Military Specialized Sciences, 2(35), 29–32. https://doi.org/10.17721/1728-2217.2016.35.29-32 [in Ukrainian].
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Дмитро ЛЯШЕНКО, Іванна ЗОБНІВ, Олександр ЦВИК
This work is licensed under a Creative Commons Attribution 4.0 International License.
Read the policy here: https://geology.bulletin.knu.ua/licensing
