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MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis

Majedaldein Almahasneh, Adeline Paiement Orcid Logo, Xianghua Xie Orcid Logo, Jean Aboudarham Orcid Logo

SN Computer Science, Volume: 3, Issue: 3

Swansea University Authors: Majedaldein Almahasneh, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1007/s42979-022-01088-y

Abstract

Precisely detecting solar active regions (AR) from multi-spectral images is a challenging task yet important in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of these 3D objects, as opposed to more traditiona...

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Published in: SN Computer Science
ISSN: 2662-995X 2661-8907
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59709
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spelling 2023-01-19T13:05:31.6321265 v2 59709 2022-03-28 MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis 8ca3681c1e01492c49612ce56fbf1c0c Majedaldein Almahasneh Majedaldein Almahasneh true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2022-03-28 SCS Precisely detecting solar active regions (AR) from multi-spectral images is a challenging task yet important in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of these 3D objects, as opposed to more traditional multi-spectral imaging scenarios where all image bands observe the same scene. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR detection where different image bands (and physical locations) each have their own set of results. Different feature fusion strategies are investigated in this work, where information from different image modalities is aggregated at different semantic levels throughout the network. This allows the network to benefit from the joint analysis while preserving the band-specific information. We compare our detection method against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs (Verbeeck et al. Astron Astrophys 561:16, 2013)) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands. We also evaluate our proposed approach on synthetic data of similar spatial configurations obtained from annotated multi-modal magnetic resonance images. Journal Article SN Computer Science 3 3 Springer Science and Business Media LLC 2662-995X 2661-8907 Object detection; Solar images; Active regions; Multi-spectral images; Deep neural networks 23 3 2022 2022-03-23 10.1007/s42979-022-01088-y COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) 2023-01-19T13:05:31.6321265 2022-03-28T10:44:18.3423514 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Majedaldein Almahasneh 1 Adeline Paiement 0000-0001-5114-1514 2 Xianghua Xie 0000-0002-2701-8660 3 Jean Aboudarham 0000-0002-0156-8162 4 59709__23679__a4070663180c4dcdac5ef3e3f3266728.pdf 59709.pdf 2022-03-28T10:47:15.8453703 Output 1867830 application/pdf Version of Record true © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
spellingShingle MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
Majedaldein Almahasneh
Xianghua Xie
title_short MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
title_full MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
title_fullStr MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
title_full_unstemmed MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
title_sort MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis
author_id_str_mv 8ca3681c1e01492c49612ce56fbf1c0c
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 8ca3681c1e01492c49612ce56fbf1c0c_***_Majedaldein Almahasneh
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Majedaldein Almahasneh
Xianghua Xie
author2 Majedaldein Almahasneh
Adeline Paiement
Xianghua Xie
Jean Aboudarham
format Journal article
container_title SN Computer Science
container_volume 3
container_issue 3
publishDate 2022
institution Swansea University
issn 2662-995X
2661-8907
doi_str_mv 10.1007/s42979-022-01088-y
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Precisely detecting solar active regions (AR) from multi-spectral images is a challenging task yet important in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of these 3D objects, as opposed to more traditional multi-spectral imaging scenarios where all image bands observe the same scene. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR detection where different image bands (and physical locations) each have their own set of results. Different feature fusion strategies are investigated in this work, where information from different image modalities is aggregated at different semantic levels throughout the network. This allows the network to benefit from the joint analysis while preserving the band-specific information. We compare our detection method against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs (Verbeeck et al. Astron Astrophys 561:16, 2013)) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands. We also evaluate our proposed approach on synthetic data of similar spatial configurations obtained from annotated multi-modal magnetic resonance images.
published_date 2022-03-23T04:17:13Z
_version_ 1763754150302056448
score 11.03559

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