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All issues Volume 539 (2024) E3S Web of Conf., 539 (2024) 01028 References
Open Access
Issue
E3S Web of Conf.
Volume 539, 2024
III International Conference on Agriculture, Earth Remote Sensing and Environment (RSE-III-2024)
Article Number 01028
Number of page(s) 9
Section Ecology, Environmental Protection and Conservation of Biological Diversity
DOI https://doi.org/10.1051/e3sconf/202453901028
Published online 17 June 2024
  1. N. Gahlot, Mr.Dhara, G. Prusty, Accuracy evaluation of various satellite imagery products for large scale topographic mapping, XLII, 105–110 (2018) [Google Scholar]
  2. N. Abramov, D. Makarov, A. Talalaev, V. Fralenko, Modern methods for intelligent processing of Earth remote sensing data, Program Systems: Theory and Applications, 9, 417–442 (2018) [CrossRef] [Google Scholar]
  3. O. Gvozdeva, E. Kolbneva, S. Kuznetsova, N. Ruleva, I. Chuksin, Information support for monitoring urban lands to regulate land use in order to improve the quality of the natural environment, IOP Conference Series: Earth and Environmental Science, 867, 012167 (2021) [CrossRef] [Google Scholar]
  4. Gu Zhujun, Maimai Zeng, The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection, Review and Perspectives, Sustainability, 6, 274 (2023) [Google Scholar]
  5. A. Nawaz, Z. Iqbal, S. Ullah, Performance analysis of supervised image classification techniques for the classification of multispectral satellite imagery, 1–5, 10.1109/ICASE, 7489513 (2015) [Google Scholar]
  6. A. Asokan, J. Anitha, Change detection techniques for remote sensing applications: a survey, Earth Sci Inform, 12, 143–160 (2019) [CrossRef] [Google Scholar]
  7. R.G. Asadullaev, N.I. Kuzmenko, Technology of intelligent agricultural crops recognition by neural network based on multispectral multitemporal Earth remote sensing data, Economics, Information technologies, 49, 1, 159–168 (2022) [Google Scholar]
  8. A. Gafurov, Sv. Muharamova, A. Saveliev, O. Yermolaev, Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis. Agriculture, 13, 1672 (2023) [CrossRef] [Google Scholar]
  9. N. Khanduri, S. Akhtar, S. Vashisht, S. Gupta, An enhancement to satellite image processing resolution. Amity J. Comput. Sci. (AJCS), 4, 1 (2020) [Google Scholar]
  10. S.Yu. Miroshnichenko, V.S. Titov, A.A. Yashchenko, The method of automatic localization of extended geospatial objects in satellite images, Proceedings of higher educational institutions. Instrumentation, 6, 17–22 (2013) [Google Scholar]
  11. T.I. Mikheeva, Data Mining in geoinformation technologies, Bulletin of SamSTU, Series Technical Sciences, 41, 96–99 (2006) [Google Scholar]
  12. B. Sumbangan, S. Arif, Surni Nurmiaty, A. Fauzan Adzima, R. Mawaleda, Geoportal platform design for agricultural development of small island based on geosmart system, IOP Conference Series: Earth and Environmental Science, 1107, 012066 (2022) [CrossRef] [Google Scholar]
  13. G.P. Obi Reddy, S. Singh, Geospatial Technologies in Land Resources Mapping, Monitoring and Management (2018) [Google Scholar]
  14. I. Cáceres-Criado, P. Trivino-Tarradas, Jose Valderrama-Zafra, D. García-Molina, Proposition for the graphic representation, interpretation and evaluation of the degree of terrain resolution in virtual reconstructions, Journal of Cultural Heritage, 63 (2023) [Google Scholar]
  15. E. Markov, Fractal methods for extracting artificial objects from theunmanned aerial vehicle images, Journal of Applied Remote Sensing, 10, 2 (2016) [Google Scholar]
  16. W. Sun, G. Xu, P. Gong, S. Liang, Fractal analysis of remotely sensedimages: A review of methods and applications, International Journal of Remote Sensing, 27, 22 (2007) [Google Scholar]
  17. A. Kashnitskii, E. Loupian, I. Balashov, A. Konstantinova, Technology for designing tools for the process and analysis of data from very large scale distributed satellite archives, Atmospheric and Oceanic Optics, 30, 84–88 (2017) [CrossRef] [Google Scholar]
  18. Sentinel-1 Level Detailed Algorithm Definition. Document Number: SEN-TN-52-7445. S-1 MPC Nomenclature: DI-MPC-IPFDPM (2016) [Google Scholar]
  19. V.B.H. Ketelaar (Gini), Satellite Radar Interferometry: Subsidence Monitoring Techniques, Remote Sensing and Digital Image Processing (Springer Berlin, 2009) [Google Scholar]
  20. M.V. Priya, K. Rengabashyam, S. Pazhanivelan, K. Ramalingam, R. Kaliaperumal, G. Vanitha, A. Nihar, P.J. Prajesh, V. Venkatesan, Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu, Journal of Applied and Natural Science, 15, 1170–1177 (2023) [CrossRef] [Google Scholar]
  21. G. Novando, D. Arif, Comparison of soil adjusted vegetation index (SAVI) and modified soil adjusted vegetation index (MSAVI) methods to view vegetation density in padang city using landsat 8 image, International Remote Sensing Applied Journal, 2, 31–36 (2023) [CrossRef] [Google Scholar]
  22. N. Teshaev, B. Mamadaliyev, A. Ibragimov, S. Khasanov, The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan, InterCarto, InterGIS, 26 (2020) [Google Scholar]

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