Deep Transfer Learning of Satellite Imagery for Land Use and Land Cover Classification

Deep Transfer Learning of Satellite Imagery for Land Use and Land Cover Classification

Deep learning has been instrumental in solving difficult problems by automatically learning, from sample data, the rules (algorithms) that map an input to its respective output. Purpose: Perform land use landcover (LULC) classification using the training data of satellite imagery for Moscow region a...

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Bibliographic Details
Main Authors:	Teklay Yifter, Yury Razoumny, Vasiliy Lobanov
Format:	Article
Language:	English
Published:	Russian Academy of Sciences, St. Petersburg Federal Research Center 2022-09-01
Series:	Информатика и автоматизация
Subjects:	neural networks deep transfer learning land use land cover classification satellite imagery
Online Access:	http://ia.spcras.ru/index.php/sp/article/view/15395

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