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Removing cloud shadows from ground-based solar imagery

Amal Chaoui, Jay Morgan Orcid Logo, Adeline Paiement, Jean Aboudarham

Machine Vision and Applications, Volume: 35, Issue: 6

Swansea University Author: Jay Morgan Orcid Logo

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DOI (Published version): 10.1007/s00138-024-01607-2

Abstract

The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows...

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Published in: Machine Vision and Applications
ISSN: 0932-8092 1432-1769
Published: Springer Science and Business Media LLC 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67663
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spelling v2 67663 2024-09-12 Removing cloud shadows from ground-based solar imagery df9a27bcf77b4769c2ebbb702b587491 0000-0003-3719-362X Jay Morgan Jay Morgan true false 2024-09-12 MACS The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds. Quantitative assessments are obtained through image quality indices (RMSE, PSNR, SSIM, and FID). We demonstrate improved results with regards to the traditional cloud removal technique and a sparse coding baseline, on different cloud types and textures. Journal Article Machine Vision and Applications 35 6 Springer Science and Business Media LLC 0932-8092 1432-1769 1 11 2024 2024-11-01 10.1007/s00138-024-01607-2 http://dx.doi.org/10.1007/s00138-024-01607-2 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) Agence National de la Recherche ANR grant No ANR-20-CE23-0014-01 2024-09-24T12:05:18.8204706 2024-09-12T13:32:02.7306620 College of Science Computer Science Amal Chaoui 1 Jay Morgan 0000-0003-3719-362X 2 Adeline Paiement 3 Jean Aboudarham 4 67663__31448__589e0b2cdef6451fa8123dfccde4df41.pdf 67663.VOR.pdf 2024-09-24T11:57:25.7928460 Output 3729825 application/pdf Version of Record true This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. true eng http://creativecommons.org/licenses/by/4.0/ 273 Jay Paul Morgan 0000-0003-3719-362X j.p.morgan@swansea.ac.uk true 10.5281/zenodo.8010703 false
title Removing cloud shadows from ground-based solar imagery
spellingShingle Removing cloud shadows from ground-based solar imagery
Jay Morgan
title_short Removing cloud shadows from ground-based solar imagery
title_full Removing cloud shadows from ground-based solar imagery
title_fullStr Removing cloud shadows from ground-based solar imagery
title_full_unstemmed Removing cloud shadows from ground-based solar imagery
title_sort Removing cloud shadows from ground-based solar imagery
author_id_str_mv df9a27bcf77b4769c2ebbb702b587491
author_id_fullname_str_mv df9a27bcf77b4769c2ebbb702b587491_***_Jay Morgan
author Jay Morgan
author2 Amal Chaoui
Jay Morgan
Adeline Paiement
Jean Aboudarham
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container_title Machine Vision and Applications
container_volume 35
container_issue 6
publishDate 2024
institution Swansea University
issn 0932-8092
1432-1769
doi_str_mv 10.1007/s00138-024-01607-2
publisher Springer Science and Business Media LLC
college_str College of Science
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hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
url http://dx.doi.org/10.1007/s00138-024-01607-2
document_store_str 1
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description The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds. Quantitative assessments are obtained through image quality indices (RMSE, PSNR, SSIM, and FID). We demonstrate improved results with regards to the traditional cloud removal technique and a sparse coding baseline, on different cloud types and textures.
published_date 2024-11-01T12:05:17Z
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