Identification of vegetation types and its boundaries using artificial neural networks : доклад, тезисы доклада

Saltykov M; Yakubailik O; Bartsev S

Дополнительные ссылки: РИНЦ · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: Identification of vegetation types and its boundaries using artificial neural networks : доклад, тезисы доклада / M. M. Saltykov, O. O. Yakubailik, S. S. Bartsev. - Текст : непосредственный // IOP Conference Series: Materials Science and Engineering : International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019" / Krasnoyarsk Science and Technology City Hall of the Russian Union of Scientific and Engineering Associations ; International Scientific Workshop «MIP Engineering 2019: Advanced Technologies in Material Science, Mechanical and Automation Engineering» (2019 ; 04.04 - 06.04 ; Krasnoyarsk) : Institute of Physics and IOP Publishing Limited, 2019. - 537. - P. 62001.

Аннотация: The applicability of artificial neural networks (ANN) for the identification of vegetation types using satellite multispectral imagery was studied. The study was focused on the three main vegetation types found in the south of the Krasnoyarsk Region: mixed forest, boreal forest and grassland. Sentinel-2 satellite images were used as a data source for the neural networks. It was shown that vegetation type can be identified pixel-by-pixel using 12 spectral channels and simple feed forward ANN with good quality and reliability. Analysis of the input layer of the trained neural networks allowed several spectral bands to be selected that were the most valuable for the ANN decision and not used in the classic NDVI vegetation index.

Год издания: 2019

Авторы: Saltykov M , Yakubailik O , Bartsev S

Издательство: Institute of Physics and IOP Publishing Limited

Источник: IOP Conference Series: Materials Science and Engineering

Выпуск: 537

Мероприятие: International Scientific Workshop «MIP Engineering 2019: Advanced Technologies in Material Science, Mechanical and Automation Engineering» (Krasnoyarsk, 2019 ; 04.04 - 06.04)

Номера страниц: 62001

Держатель оригинала документа: Institute of Biophysics FRC KSC SB RAS, Institute of Computation modeling FRC KSC SB RAS

Количество экземпляров:

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