Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103182
Title: Super-resolution of satellite images: Feasibility of deep learning techniques
Authors: Akimova, E. N.
Deikov, A. A.
Issue Date: 2020
Publisher: American Institute of Physics Inc.
Citation: Akimova E. N. Super-resolution of satellite images: Feasibility of deep learning techniques / E. N. Akimova, A. A. Deikov. — DOI 10.1063/5.0026613 // AIP Conference Proceedings. — 2020. — Vol. 2293. — 140002.
Abstract: The work is devoted to studying the feasibility of applying the convolutional neural networks with deep learning to the problems of super-resolution of satellite images. The main aim is to enhance the image details and delete the artifacts. The algorithms for resolution enhancement were studied. The training set of satellite images was prepared. The neural network was constructed and trained using the PyTorch library for the Python language and the NVIDIA Tesla K40m graphics processors. Comparison of constructed network with the classic interpolation algorithms was carried out for the reference satellite images. It was shown that the neural network gives a better quality of the images. © 2020 American Institute of Physics Inc.. All rights reserved.
Keywords: CUD A
DEEP LEARNING
NEURAL NETWORK
PY TORCH
PYTHON
SUPER-RESOLUTION
URI: http://hdl.handle.net/10995/103182
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85098012179
PURE ID: 20362490
3e65533a-63c6-4769-843d-361e61c69456
ISSN: 0094243X
ISBN: 9780735440258
DOI: 10.1063/5.0026613
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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