Issue |
E3S Web Conf.
Volume 592, 2024
International Scientific Conference Energy Management of Municipal Facilities and Environmental Technologies (EMMFT-2024)
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Article Number | 05006 | |
Number of page(s) | 8 | |
Section | Mining, Geology, Geodesy, and Environmental Monitoring | |
DOI | https://doi.org/10.1051/e3sconf/202459205006 | |
Published online | 20 November 2024 |
Enhancing super-resolution in remote sensing: Integrating GIS data with CNN-based SRGAN models for improved image reconstruction
Northern (Arctic) Federal University named after M.V. Lomonosov, Arkhangelsk, Russia
* Corresponding author: v.berezovsky@narfu.ru
In the field of remote sensing (RS), image super-resolution (SR) techniques play a crucial role across various applications. Traditional SR methods face challenges when applied to long-term coverage datasets with limited spatial resolution. However, recent advancements in deep learning have opened up new possibilities for improving the spatial resolution of RS data. While many convolutional neural network (CNN)- based approaches have achieved excellent performance in developing efficient end-to-end SR models for natural images, they have been less frequently applied to satellite image upscaling with high scale factors. This paper introduces a novel CNN block that enhances the performance of SRGAN-based models. Experimental results show that these architectures benefit from additional data, especially when low-resolution images provide insufficient feature information.
© 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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