Nina Pajević je rođena u Novom Sadu 1997. god. Osnovne akademske studije na Departmanu za energetiku, elektroniku i telekomunikacije, Fakultet tehničkih nauka, Univerzitet u Novom Sadu, smer Obrada signala, uspešno je završila 2020. god. Na istom fakultetu i studijskom programu upisuje i master akademske studije i 2021. godine stiče uslov za odbranu diplomskog-master rada.
Univerzitet u Novom Sadu, Fakultet tehničkih nauka
The paper describes the segmentation of Sentinel-2 satellite images based on derived vegetation indices in order to map stress-affected regions of agricultural land. The database consists of a time series of multispectral images of 48 agricultural parcels in Vojvodina. The time series includes images for 13 dates in the period from March to September 2020. Three segmentation methods were applied: threshold segmentation, segmentation using the k-means algorithm, and segmentation using the pyImSegm library. The first damaged area was not successfully detected by segmentation with the first method, while the other two methods showed a good ability to map less developed regions.
References
[1] Zheng, Q., Huang, W., Cui, X., Shi, Y. and Liu, L., 2018. New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery. Sensors, 18(3), p.868.
[2] Sentinel-2 User Handbook, https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook
(pristupljeno u septembru 2021.)
[3] Chemura, A., Mutanga, O. and Dube, T., 2016. Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precision Agriculture, 18(5), pp.859-881.
[4]https://stanford.edu/~cpiech/cs221/handouts/kmeans.html (pristupljeno u septembru 2021.)
[5]https://www.mathworks.com/help/images/ref/superpixels.html (pristupljeno u septembru 2021.)
[6] Borovec, J., Švihlík, J., Kybic, J. and Habart, D., 2017. Supervised and unsupervised segmentation using superpixels, model estimation, and graph cut. Journal of Electronic Imaging, 26(06), p.1.