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Start Over Descriptor "Solar Images" Topic active regions
2 results on '"Solar Images"'

2. Active region detection in multi-spectral solar images

Author

Xianghua Xie, Jean Aboudarham, Adeline Paiement, Majedaldein Almahasneh, Department of Computer Science [Swansea], Swansea University, DYNamiques de l’Information (DYNI), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Observatoire de Paris, Université Paris sciences et lettres (PSL), Laboratoire d'études spatiales et d'instrumentation en astrophysique = Laboratory of Space Studies and Instrumentation in Astrophysics (LESIA), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Paiement, Adeline, and Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [PHYS.ASTR.IM]Physics [physics]/Astrophysics [astro-ph]/Instrumentation and Methods for Astrophysic [astro-ph.IM], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Joint Analysis, Multi-spectral Images, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Coronal hole, Multi spectral, 02 engineering and technology, Space weather, Solar Images, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Image (mathematics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Active Regions, Modality (human–computer interaction), [PHYS.ASTR.SR] Physics [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR], business.industry, Deep learning, Region detection, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [PHYS.ASTR.SR]Physics [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR], [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, [PHYS.ASTR.IM] Physics [physics]/Astrophysics [astro-ph]/Instrumentation and Methods for Astrophysic [astro-ph.IM], business
Abstract

International audience; 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. We compare our detection method against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs (Verbeeck et al., 2013)) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands.

Published
2021

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