Cloud Removal in Satellite Images Using a Generative Adversarial Approach

Automation of a wide range of operations in the agro-industrial sector at the current stage of technology development involves the application of advanced solutions in electronics, biotechnology, and information technologies. One of the directions of process optimization in agriculture is the introd...

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Главные авторы:	Nikolaev, E. I., Николаев, Е. И., Zakharova, N. I., Захарова, Н. И.
Формат:	Статья
Язык:	English
Опубликовано:	Institute of Electrical and Electronics Engineers Inc. 2025
Темы:	Agricultural information systems Deep learning Cloud detection Cloud removal Data augmentation GAN
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/30654
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spelling	ir-123456789-306542025-07-02T09:45:13Z Cloud Removal in Satellite Images Using a Generative Adversarial Approach Nikolaev, E. I. Николаев, Е. И. Zakharova, N. I. Захарова, Н. И. Agricultural information systems Deep learning Cloud detection Cloud removal Data augmentation GAN Automation of a wide range of operations in the agro-industrial sector at the current stage of technology development involves the application of advanced solutions in electronics, biotechnology, and information technologies. One of the directions of process optimization in agriculture is the introduction of information systems functioning on the basis of satellite imagery data analysis. To improve the efficiency of satellite data application, it is advisable to use machine learning and artificial intelligence methods. Satellite data allow monitoring a set of indicators describing chemical and physical characteristics, soil types, weather conditions, humidity. These indicators play an important role in the decision-making process of smart farming systems. The indicators are available in the form of satellite images, the quality of which depends on many factors. In order to apply such images in agricultural information systems, it is necessary to perform deep image analysis and cleaning. The approach aimed at applying a generative deep neural network to clean satellite images from clouds and shadows is proposed. The approach is based on training the neural network on synthesized data. 2025-07-02T09:44:24Z 2025-07-02T09:44:24Z 2025 Статья Nikolaev E., Zakharova N., Zakharov V. Cloud Removal in Satellite Images Using a Generative Adversarial Approach // Proceedings - 2025 International Russian Smart Industry Conference, SmartIndustryCon 2025. - 2025. - pp. 614 - 618. - DOI: 10.1109/SmartIndustryCon65166.2025.10986034 https://dspace.ncfu.ru/handle/123456789/30654 en Proceedings - 2025 International Russian Smart Industry Conference, SmartIndustryCon 2025 application/pdf Institute of Electrical and Electronics Engineers Inc.
institution	СКФУ
collection	Репозиторий
language	English
topic	Agricultural information systems Deep learning Cloud detection Cloud removal Data augmentation GAN
spellingShingle	Agricultural information systems Deep learning Cloud detection Cloud removal Data augmentation GAN Nikolaev, E. I. Николаев, Е. И. Zakharova, N. I. Захарова, Н. И. Cloud Removal in Satellite Images Using a Generative Adversarial Approach
description	Automation of a wide range of operations in the agro-industrial sector at the current stage of technology development involves the application of advanced solutions in electronics, biotechnology, and information technologies. One of the directions of process optimization in agriculture is the introduction of information systems functioning on the basis of satellite imagery data analysis. To improve the efficiency of satellite data application, it is advisable to use machine learning and artificial intelligence methods. Satellite data allow monitoring a set of indicators describing chemical and physical characteristics, soil types, weather conditions, humidity. These indicators play an important role in the decision-making process of smart farming systems. The indicators are available in the form of satellite images, the quality of which depends on many factors. In order to apply such images in agricultural information systems, it is necessary to perform deep image analysis and cleaning. The approach aimed at applying a generative deep neural network to clean satellite images from clouds and shadows is proposed. The approach is based on training the neural network on synthesized data.
format	Статья
author	Nikolaev, E. I. Николаев, Е. И. Zakharova, N. I. Захарова, Н. И.
author_facet	Nikolaev, E. I. Николаев, Е. И. Zakharova, N. I. Захарова, Н. И.
author_sort	Nikolaev, E. I.
title	Cloud Removal in Satellite Images Using a Generative Adversarial Approach
title_short	Cloud Removal in Satellite Images Using a Generative Adversarial Approach
title_full	Cloud Removal in Satellite Images Using a Generative Adversarial Approach
title_fullStr	Cloud Removal in Satellite Images Using a Generative Adversarial Approach
title_full_unstemmed	Cloud Removal in Satellite Images Using a Generative Adversarial Approach
title_sort	cloud removal in satellite images using a generative adversarial approach
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2025
url	https://dspace.ncfu.ru/handle/123456789/30654
work_keys_str_mv	AT nikolaevei cloudremovalinsatelliteimagesusingagenerativeadversarialapproach AT nikolaevei cloudremovalinsatelliteimagesusingagenerativeadversarialapproach AT zakharovani cloudremovalinsatelliteimagesusingagenerativeadversarialapproach AT zaharovani cloudremovalinsatelliteimagesusingagenerativeadversarialapproach
_version_	1842245594757726208

Cloud Removal in Satellite Images Using a Generative Adversarial Approach

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