Have a personal or library account? Click to login
Exact Algorithms for the Satellite Image Selection Problem Cover

References

  1. Airbus (2023a). Pléiades, https://www.intelligenceairbusds.com/en/8692-pleiades.
  2. Airbus (2023b). Pléiades neo, https://www.airbus.com/en/products-services/space/earth-observation/earth-observation-portfolio/pleiades-neo.
  3. Airbus (2023c). SPOT, https://www.intelligence-airbusds.com/en/8693-spot-67.
  4. Asner, G.P., Powell, G.V.N., Mascaro, J., Knapp, D.E., Clark, J.K., Jacobson, J., Kennedy-Bownoten, T., Balaji, A., Paez-Acosta, G., Victoria, E., Secada, L., Valqui, M. and Hughes, R.F. (2010). High-resolution forest carbon stocks and emissions in the Amazon, Proceedings of the National Academy of Sciences 107(38): 16738–16742, DOI: 10.1073/pnas.1004875107.
  5. Bennett, M.M. and Smith, L.C. (2017). Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics, Remote Sensing of Environment 192: 176–197, DOI: 10.1016/j.rse.2017.01.005.
  6. Błazewicz, J., Kovalyov, M., Musiał, J., Urbański, A. and Wojciechowski, A. (2010). Internet shopping optimization problem, International Journal of Applied Mathematics and Computer Science 20(2): 385–390, DOI: 10.2478/v10006-010-0028-0.
  7. Burke, M., Driscoll, A., Lobell, D.B. and Ermon, S. (2021). Using satellite imagery to understand and promote sustainable development, Science 371(6535): eabe8628, DOI: 10.1126/science.abe8628.
  8. Chen, K., Luo, W., Lin, X., Song, Z. and Chang, Y. (2024). Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons, IEEE Transactions on Emerging Topics in Computational Intelligence 8(3): 2433–2445, DOI: 10.1109/TETCI.2024.3361755.
  9. Combarro Simón, M., Talbot, P., Danoy, G., Musial, J., Alswaitti, M. and Bouvry, P. (2023). Constraint Model for the Satellite Image Mosaic Selection Problem, LIPIcs, Volume 280, CP 2023 280: 44:1–44:15, DOI: 10.4230/LIPICS.CP.2023.44.
  10. Cygan, M., Fomin, F.V., Kowalik, Ł., Lokshtanov, D., Marx, D., Pilipczuk, M., Pilipczuk, M. and Saurabh, S. (2015). Parameterized Algorithms, Springer, Cham, DOI: 10.1007/978-3-319-21275-3.
  11. Felegari, S., Sharifi, A., Moravej, K., Amin, M., Golchin, A., Muzirafuti, A., Tariq, A. and Zhao, N. (2021). Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping, Applied Sciences 11(21): 10104, DOI: 10.3390/app112110104.
  12. Flood, N., Watson, F. and Collett, L. (2019). Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia, International Journal of Applied Earth Observation and Geoinformation 82: 101897, DOI: 10.1016/j.jag.2019.101897.
  13. Goetz, S. (2007). Crisis in Earth Observation, Science 315(5820): 1767–1767, DOI: 10.1126/science.1142466.
  14. Hall, K., Reitalu, T., Sykes, M.T. and Prentice, H.C. (2012). Spectral heterogeneity of QuickBird satellite data is related to fine-scale plant species spatial turnover in semi-natural grasslands, Applied Vegetation Science 15(1): 145–157, DOI: 10.1111/j.1654-109X.2011.01143.x.
  15. Hansen, M.C., Stehman, S.V., Potapov, P.V., Loveland, T.R., Townshend, J. R.G., DeFries, R.S., Pittman, K.W., Arunarwati, B., Stolle, F., Steininger, M.K., Carroll, M. and DiMiceli, C. (2008). Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data, Proceedings of the National Academy of Sciences 105(27): 9439–9444, DOI: 10.1073/pnas.0804042105.
  16. Henderson, J.V., Storeygard, A. and Weil, D.N. (2012). Measuring Economic Growth from Outer Space, American Economic Review 102(2): 994–1028, DOI: 10.1257/aer.102.2.994.
  17. Huangfu, Q. and Hall, J.A.J. (2018). Parallelizing the dual revised simplex method, Mathematical Programming Computation 10(1): 119–142, DOI: 10.1007/s12532-017-0130-5.
  18. James, M. R. and Robson, S. (2014). Mitigating systematic error in topographic models derived from UAV and ground-based image networks, Earth Surface Processes and Landforms 39(10): 1413–1420, DOI: 10.1002/esp.3609.
  19. Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B. and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty, Science 353(6301): 790–794, DOI: 10.1126/science.aaf7894.
  20. Lopez-Loces, M.C., Musial, J., Pecero, J.E., Fraire-Huacuja, H.J., Blazewicz, J. and Bouvry, P. (2016). Exact and heuristic approaches to solve the Internet shopping optimization problem with delivery costs, International Journal of Applied Mathematics and Computer Science 26(2): 391–406, DOI: 10.1515/amcs-2016-0028.
  21. Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M. and Pyšek, P. (2017). Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring, Frontiers in Plant Science 8: 887, DOI: 10.3389/fpls.2017.00887.
  22. Rovetto, R.J. (2017). An ontology for satellite databases, Earth Science Informatics 10(4): 417–427, DOI: 10.1007/s12145-017-0290-x.
  23. Sánchez-Azofeifa, A., Rivard, B., Wright, J., Feng, J.-L., Li, P., Chong, M.M. and Bohlman, S.A. (2011). Estimation of the Distribution of Tabebuia guayacan (Bignoniaceae) Using High-Resolution Remote Sensing Imagery, Sensors 11(4): 3831–3851, DOI: 10.3390/s110403831.
  24. Shean, D.E., Alexandrov, O., Moratto, Z.M., Smith, B.E., Joughin, I.R., Porter, C. and Morin, P. (2016). An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery, ISPRS Journal of Photogrammetry and Remote Sensing 116: 101–117, DOI: 10.1016/j.isprsjprs.2016.03.012.
  25. Tian, H., Pei, J., Huang, J., Li, X., Wang, J., Zhou, B., Qin, Y. and Wang, L. (2020). Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China, Remote Sensing 12(21): 3539, DOI: 10.3390/rs12213539.
  26. Verpoorter, C., Kutser, T., Seekell, D.A. and Tranvik, L.J. (2014). A global inventory of lakes based on high-resolution satellite imagery, Geophysical Research Letters 41(18): 6396–6402, DOI: 10.1002/2014GL060641.
  27. Virtanen, P. et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python, Nature Methods 17(3): 261–272, DOI: 10.1038/s41592-019-0686-2.
  28. Wang, Y., Zhang, D. and Dai, G. (2020). Classification of high resolution satellite images using improved U–Net, International Journal of Applied Mathematics and Computer Science 30(3): 399–413, DOI: 10.34768/amcs-2020-0030.
  29. Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S. and Burke, M. (2020). Using publicly available satellite imagery and deep learning to understand economic well-being in Africa, Nature Communications 11(1): 2583, DOI: 10.1038/s41467-020-16185-w.
  30. Zok, T., Badura, J., Swat, S., Figurski, K., Popenda, M. and Antczak, M. (2020). New models and algorithms for RNA pseudoknot order assignment, International Journal of Applied Mathematics and Computer Science 30(2): 315–324, DOI: 10.34768/amcs-2020-0024.
DOI: https://doi.org/10.61822/amcs-2025-0021 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 293 - 309
Submitted on: Jun 26, 2024
Accepted on: Nov 15, 2024
Published on: Jun 24, 2025
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Sylwester Swat, Maciej Antczak, Tomasz Zok, Jacek Blazewicz, Jedrzej Musial, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.