1. Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666-20671. [ DOI:10.1073/pnas.0704119104] 2. Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28(1), 205-241. [ DOI:10.1146/annurev.energy.28.050302.105459] 3. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. [ DOI:10.1080/01431160600746456] 4. Drusch, M., et al. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25-36. [ DOI:10.1016/j.rse.2011.11.026] 5. Torres, R., et al. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9-24. [ DOI:10.1016/j.rse.2011.05.028] 6. Claverie, M., et al. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. [ DOI:10.1016/j.rse.2018.09.002] 7. Ban, Y., Jacob, A., & Gamba, P. (2015). Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 28-37.
https://doi.org/10.1016/j.isprsjprs.2014.08.004 [ DOI:10.1016/j.isprsjprs.2015.01.011] 8. Hafner, M., et al. (2020). Semi-supervised change detection using Sentinel-1 and Sentinel-2 time series. Remote Sensing, 12(3), 456. [ DOI:10.3390/rs12030456] 9. Zhang, X., et al. (2021). GLC_FCS10: A 10 m resolution global land cover dataset based on Sentinel imagery. Remote Sensing, 13(5), 922. [ DOI:10.3390/rs13050922] 10. Coppin, P., & Bauer, M. (1996). Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3-4), 207-234. [ DOI:10.1080/02757259609532305] 11. Zhu, X. X., et al. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36. [ DOI:10.1109/MGRS.2017.2762307] 12. Chen, Y., et al. (2021). OFNet: Optical flow guided network with dual attention for change detection. Remote Sensing, 13(2), 254. [ DOI:10.3390/rs13020254] 13. Chen, J., et al. (2003). A simple method for detecting change in remotely sensed images by using the change vector analysis. International Journal of Remote Sensing, 24(8), 1657-1666. [ DOI:10.1080/01431160210163095] 14. Li, X., et al. (2019). Urban change detection using Sentinel-1 data and support vector machines. Remote Sensing Letters, 10(5), 451-460. [ DOI:10.1080/2150704X.2019.1585183] 15. Johnson, B. A., et al. (2020). Land cover classification in Africa using Sentinel-2 and artificial neural networks. Remote Sensing, 12(3), 456. [ DOI:10.3390/rs12030456] 16. Davis, C., et al. (2019). Evaluating CNN architectures for satellite image classification. ISPRS International Journal of Geo-Information, 8(5), 219. [ DOI:10.3390/ijgi8050219] 17. Clark, M., et al. (2021). Improving urban land cover classification using optimized U-Net architecture. Remote Sensing, 13(10), 1987. [ DOI:10.3390/rs13101987] 18. Fawzy, M., & Barsi, Á. (2022). Urban land cover classification using high-resolution imagery and U-Net. Remote Sensing, 14(3), 678. [ DOI:10.3390/rs14030678] 19. Alkhediri, M., et al. (2018). Object-based change detection using PCA and high-resolution imagery. Remote Sensing Letters, 9(2), 123-132. [ DOI:10.1080/2150704X.2017.1410292] 20. Peterson, J., et al. (2020). Deep learning for urban change detection: A comparative study. Remote Sensing, 12(6), 1012. [ DOI:10.3390/rs12061012] 21. Zhang, Y., et al. (2021). Multi-temporal urban expansion analysis using machine learning. Remote Sensing, 13(7), 1356. [ DOI:10.3390/rs13071356] 22. Ghorbanzadeh, O., et al. (2020). Fusion of Sentinel-1 and Sentinel-2 data for urban change detection. Remote Sensing, 12(4), 678. [ DOI:10.3390/rs12040678] 23. Li, Y., et al. (2022). Siamese networks for fine-grained urban change detection. Remote Sensing, 14(1), 123. [ DOI:10.3390/rs14010123] 24. Hafner, M. (2021). Multi-modal learning for urban change detection using Sentinel data (Doctoral dissertation). [University Repository] 25. Mastro, L., et al. (2020). Urban and disaster change detection using random forest and Sentinel-1. Remote Sensing, 12(9), 1456. [ DOI:10.3390/rs12091456] 26. Zhang, X., et al. (2022). Urban change detection using CNN and Sentinel-2 imagery. Remote Sensing, 14(2), 456. [ DOI:10.3390/rs14020456] 27. نویسندگان مقاله ایرانی]. (سال). طبقهبندی پوشش زمین مبتنی بر روشهای یادگیری ماشین و یادگیری عمیق با استفاده از تصاویر ماهوارهای سنتینل-۲؛ مطالعه موردی: منطقه شهری غرب تهران. مجله علم و فنون. 28. ESA. (2020). Sentinel-2 User Handbook. European Space Agency. 29. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309. 30. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. [ DOI:10.1016/0034-4257(88)90106-X] 31. ESA. (2021). Sentinel-1 Toolbox - SNAP Documentation. European Space Agency. 32. Lopes, A., Touzi, R., & Nezry, E. (1990). Adaptive speckle filters and scene heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 28(6), 992-1000. [ DOI:10.1109/36.62623] 33. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. [ DOI:10.1109/TSMC.1973.4309314] 34. Zhang, Y., & Mishra, R. (2012). A review and comparison of commercially available SAR image fusion techniques. International Journal of Remote Sensing, 33(14), 4434-4460. [35] Pohl, C., & Van Genderen, J. L. (1998). Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823-854. [ DOI:10.1080/014311698215748] 35. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. [ DOI:10.1080/01431160600746456] 36. Malila, W. A. (1980). Change vector analysis: An approach for detecting forest changes with Landsat. Proceedings of the 6th Annual Symposium on Machine Processing of Remotely Sensed Data, Purdue University, 326-335. 37. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594. [ DOI:10.1080/01431160304987] 38. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. [ DOI:10.1016/0034-4257(79)90013-0] 39. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107. [ DOI:10.1016/0034-4257(95)00186-7] 40. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033. [ DOI:10.1080/01431160600589179] 41. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., & Kuemmerle, T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70. [ DOI:10.3390/rs8010070] 42. Mahyouba, S., Fadil, A., Mansour, E. M., Rhinane, H., & Al-Nahmi, F. (2019). Fusing of optical and synthetic aperture radar (SAR) remote sensing data: A systematic literature review. ISPRS Archives, XLII-4/W12, 127-134. [ DOI:10.5194/isprs-archives-XLII-4-W12-127-2019] 43. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, [ DOI:10.1016/0034-4257(95)00186-7]
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