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Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images. / Zraenko, Sergey.
Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. стр. 1-4.

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Harvard

Zraenko, S 2023, Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images. в Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023: book. Institute of Electrical and Electronics Engineers Inc., стр. 1-4, 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023, 17/07/2023. https://doi.org/10.1109/ITNT57377.2023.10139149

APA

Zraenko, S. (2023). Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images. в Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023: book (стр. 1-4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNT57377.2023.10139149

Vancouver

Zraenko S. Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images. в Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. стр. 1-4 doi: 10.1109/ITNT57377.2023.10139149

Author

Zraenko, Sergey. / Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images. Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. стр. 1-4

BibTeX

@inproceedings{a9e23dae4373476186e28eef21124cdd,
title = "Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images",
abstract = "The results of the study of the classification procedure of coniferous and deciduous, as well as coniferous and mixed (in equal percentages) vegetation according to Landsat-7 images for different seasons of the year are presented. Spectral channels with a spatial resolution of 30 meters were used. Vegetation classification was carried out by brightness characteristics using the nearest neighbor method. The reference brightness of objects (simple standards) was determined by their mathematical expectations in each spectral channel for each season. Additionally, aggregated standards are formed by combining the brightness of plant objects in spectral channels. It is shown that when separating coniferous and deciduous objects, the probability of correct selection of coniferous can reach 1.0000 when using simple standards. At the same time, the probability of correct allocation of deciduous does not exceed 0.9697. The use of aggregated standards makes it possible to increase this probability to 0.9899. When classifying coniferous and mixed vegetation, the effectiveness of aggregated standards turned out to be lower than that of simple ones selected by spectral channels and shooting seasons. The obtained results suggest the continuation of research when dividing plant objects into a larger number of classes.",
author = "Sergey Zraenko",
note = "The work was supported by the RFBR, contract No. 19–29– 09022.; 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023 ; Conference date: 17-07-2023 Through 21-07-2023",
year = "2023",
month = apr,
day = "17",
doi = "10.1109/ITNT57377.2023.10139149",
language = "English",
isbn = "979-835039733-8",
pages = "1--4",
booktitle = "Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images

AU - Zraenko, Sergey

N1 - The work was supported by the RFBR, contract No. 19–29– 09022.

PY - 2023/4/17

Y1 - 2023/4/17

N2 - The results of the study of the classification procedure of coniferous and deciduous, as well as coniferous and mixed (in equal percentages) vegetation according to Landsat-7 images for different seasons of the year are presented. Spectral channels with a spatial resolution of 30 meters were used. Vegetation classification was carried out by brightness characteristics using the nearest neighbor method. The reference brightness of objects (simple standards) was determined by their mathematical expectations in each spectral channel for each season. Additionally, aggregated standards are formed by combining the brightness of plant objects in spectral channels. It is shown that when separating coniferous and deciduous objects, the probability of correct selection of coniferous can reach 1.0000 when using simple standards. At the same time, the probability of correct allocation of deciduous does not exceed 0.9697. The use of aggregated standards makes it possible to increase this probability to 0.9899. When classifying coniferous and mixed vegetation, the effectiveness of aggregated standards turned out to be lower than that of simple ones selected by spectral channels and shooting seasons. The obtained results suggest the continuation of research when dividing plant objects into a larger number of classes.

AB - The results of the study of the classification procedure of coniferous and deciduous, as well as coniferous and mixed (in equal percentages) vegetation according to Landsat-7 images for different seasons of the year are presented. Spectral channels with a spatial resolution of 30 meters were used. Vegetation classification was carried out by brightness characteristics using the nearest neighbor method. The reference brightness of objects (simple standards) was determined by their mathematical expectations in each spectral channel for each season. Additionally, aggregated standards are formed by combining the brightness of plant objects in spectral channels. It is shown that when separating coniferous and deciduous objects, the probability of correct selection of coniferous can reach 1.0000 when using simple standards. At the same time, the probability of correct allocation of deciduous does not exceed 0.9697. The use of aggregated standards makes it possible to increase this probability to 0.9899. When classifying coniferous and mixed vegetation, the effectiveness of aggregated standards turned out to be lower than that of simple ones selected by spectral channels and shooting seasons. The obtained results suggest the continuation of research when dividing plant objects into a larger number of classes.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85163322631

U2 - 10.1109/ITNT57377.2023.10139149

DO - 10.1109/ITNT57377.2023.10139149

M3 - Conference contribution

SN - 979-835039733-8

SP - 1

EP - 4

BT - Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023

Y2 - 17 July 2023 through 21 July 2023

ER -

ID: 41531424