Active region detection in multi-spectral solar images

Majedaldein Almahasneh (1) , Adeline Paiement (2) , Xianghua Xie (1) , Jean Aboudarham (3, 4)

1 Department of Computer Science [Swansea]
2 DYNI - DYNamiques de l’Information
3 Observatoire de Paris
4 LESIA - Laboratoire d'études spatiales et d'instrumentation en astrophysique = Laboratory of Space Studies and Instrumentation in Astrophysics

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.

Mots clés: Joint Analysis; Solar Images; Active Regions; Multi-spectral Images

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Instrumentation et méthodes pour l'astrophysique [astro-ph.IM]; Astrophysique stellaire et solaire [astro-ph.SR]

Majedaldein Almahasneh, Adeline Paiement, Xianghua Xie, Jean Aboudarham. Active region detection in multi-spectral solar images. International Conference on Pattern Recognition Applications and Methods (ICPRAM), Feb 2021, online, Austria. ⟨hal-03040990⟩