I. V. Bychkov, G. M. Ruzhnikov, R. K. Fedorov, A. K. Popova, Yu. V. Avramenko
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References
|
|
| |
| 1. |
Talukdar S, Mahato S, Shahfahad Pal S, Liou YA, Rahman A, “Land-use land-cover classification by machine learning classifiers for satellite observations–A review”, Remote Sens, 12:7 (2020), 1135  |
| 2. |
Keshtkar H, Voigt W, Alizadeh E, “Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery”, Arab J Geosci, 10:6 (2017), 154 |
| 3. |
Lastovicka J, Svec P, Paluba D, Kobliuk N, Svoboda J, Hladky R, Stych P, “Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation”, Remote Sens, 12:12 (2020), 1914 |
| 4. |
Feng Q, Liu J, Gong J, “UAV remote sensing for urban vegetation mapping using random forest and texture analysis”, Remote Sens, 7:1 (2015), 1074–1094 |
| 5. |
Liu Y, Gong W, Hu X, Gong J, “Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data”, Remote Sens, 10:6 (2018), 946  |
| 6. |
Immitzer M, Neuwirth M, Böck S, Brenner H, Vuolo F, Atzberger C, “Optimal input features for tree species classification in Central Europe based on multi-temporal Sentinel-2 data”, Remote Sens, 11:22 (2019), 2599 |
| 7. |
Axelsson A, Lindberg E, Reese H, Olsson H, “Tree species classification using Sentinel-2 imagery and Bayesian inference”, Int J Appl Earth Obs Geoinf, 100 (2021), 102318 |
| 8. |
Chambon T, Fighting hunger through open satellite data: a new state of the art for land use classification, 2019 https://medium.com/omdena/fighting-hunger-through-open-satellite-data-a-new-state-of-the-art-forland-use-classification-f57f20b7294b  |
| 9. |
Wang D, Wan B, Qiu P, Su Y, Guo Q, Wang R, Sun F, Wu X, “Evaluating the performance of Sentinel-2, Landsat 8 and Péiades-1 in mapping mangrove extent and species”, Remote Sens, 10:9 (2018), 1468 |
| 10. |
Abdi AM, “Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data”, GIScience Remote Sens, 57:1 (2020), 1–20 |
| 11. |
Paringer RA, Mukhin AV, Kupriyanov AV, “Formation of an informative index for recognizing specified objects in hyperspectral data”, Computer Optics, 45:6 (2021), 873–878 |
| 12. |
Carranza-García M, García-Gutiérrez J, Riquelme JC, “A framework for evaluating land use and land cover classification using convolutional neural networks”, Remote Sens, 11:30 (2019), 274 |
| 13. |
Campos-Taberner M, García-Haro FJ, Martínez B, Izquierdo-Verdiguier E, Atzberger C, Camps-Valls G, Gilabert MA, “Understanding deep learning in land use classification based on Sentinel-2 time series”, Sci Rep, 10:1 (2020), 17188 |
| 14. |
Ojala T, Pietikäinen M, Hawood D, “A comparative study of texture measures with classification based on featured distributions”, Pattern Recogn, 29:1 (1996), 51–59 |
| 15. |
Huang D, Shan C, Ardabilian M, Wang Y, Chen L, “Local binary patterns and its applications on facial image: A survey”, IEEE Trans Syst Man Cybern Syst, 41:6 (2011), 765–781 |
| 16. |
Myasnikov VV, “Description of images using a configuration equivalence relation”, Computer Optics, 42:6 (2018), 998–1007 |
| 17. |
Wei X, Yu X, Liu B, Zhi L, “Convolutional neural networks and local binary patterns for hyperspectral image classification”, Eur J Remote Sens, 52:1 (2019), 448–462 |
| 18. |
Zhao F, Sun R, Zhong L, Meng R, Huang C, Zeng X, Wang M, Li Y, Wang Z, “Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning”, Remote Sens Environ, 269 (2022), 112822 |
| 19. |
Bychkov IV, Ruzhnikov GM, Fedorov RK, Popova AK, Avramenko YV, “Classification of Sentinel-2 satellite images of the Baikal Natural Territory”, Computer Optics, 46:1 (2022), 90–96 |
| 20. |
Gitelson AA, Merzlyak MN, Lichtenthaler HK, “Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm”, J Plant Physiol, 148:3–4 (1996), 501–508 |
