Issue |
E3S Web Conf.
Volume 587, 2024
International Scientific Conference on Green Energy (GreenEnergy 2024)
|
|
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Article Number | 03006 | |
Number of page(s) | 8 | |
Section | Enhancing Energy Efficiency in Industry | |
DOI | https://doi.org/10.1051/e3sconf/202458703006 | |
Published online | 07 November 2024 |
Deep neural network for semantic segmentation of satellite images
Fergana branch of TUIT named after Muhammad Al-Khwarizmi, Fergana, Uzbekistan
* Corresponding author: sk363688@gmail.com
Deep neural networks have become a crucial tool for satellite image processing, particularly in semantic segmentation tasks. This paper explores the use of deep neural networks for automated feature extraction and classification in Earth satellite images. It focuses on how deep architectures like U-Net and MobileNet handle multi-channel spectral data to achieve precise segmentation of various land covers and objects of interest. The paper discusses data preprocessing techniques, loss function selection, and optimization, along with examples of successful applications in mapping, agricultural monitoring, and urban planning. The study highlights the effectiveness of deep neural networks in addressing complex satellite image segmentation challenges and showcases their potential for future research and practical use in land management and environmental monitoring.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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