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MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images

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

Machine Vision and Applications, Volume: 33, Issue: 1

Swansea University Authors: Majedaldein Almahasneh, Xianghua Xie Orcid Logo

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Abstract

Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-sp...

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Published in: Machine Vision and Applications
ISSN: 0932-8092 1432-1769
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58518
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spelling 2022-01-10T16:10:02.0721752 v2 58518 2021-10-31 MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images 8ca3681c1e01492c49612ce56fbf1c0c Majedaldein Almahasneh Majedaldein Almahasneh true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2021-10-31 SCS Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs. Journal Article Machine Vision and Applications 33 1 Springer Science and Business Media LLC 0932-8092 1432-1769 Image segmentation; object detection; deep learning; weakly supervised learning; multi-spectral images; solar image analysis; solar active regions 29 11 2021 2021-11-29 10.1007/s00138-021-01261-y COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) 2022-01-10T16:10:02.0721752 2021-10-31T17:57:26.0407111 College of Science Computer Science Majedaldein Almahasneh 1 Adeline Paiement 2 Xianghua Xie 0000-0002-2701-8660 3 Jean Aboudarham 4 58518__21791__fe030db4547846cdad4baba3193021ea.pdf 58518.pdf 2021-12-06T10:26:46.2033292 Output 1948160 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
spellingShingle MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
Majedaldein Almahasneh
Xianghua Xie
title_short MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
title_full MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
title_fullStr MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
title_full_unstemmed MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
title_sort MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images
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
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institution Swansea University
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1432-1769
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publisher Springer Science and Business Media LLC
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description Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs.
published_date 2021-11-29T04:32:55Z
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score 10.871248