Comparison of automatic methods to detect sunspots in the Coimbra Observatory spectroheliograms

Carvalho, Sara; Gomes, S.; Barata, Teresa; Lourenço, Anita; Peixinho, N.

doi:10.1016/j.ascom.2020.100385

Cited by 14 publications

(14 citation statements)

References 35 publications

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“…Watson et al, 2009Watson et al, applied his method on a 1997Watson et al, -2003 MDI continuum data set to investigate the effect of center to limb variation on sunspot appearance and disappearance. Carvalho et al, 2020 provided a comparison between intensity-based thresholding and mathematical morphology methods. It showed that mathematical morphology-based approaches perform better than purely intensity-based methods.…”

Section: Sunspot Segmentationmentioning

confidence: 99%

SunSCC: Segmenting, Grouping and Classifying Sunspots From Ground‐Based Observations Using Deep Learning

Sayez,

De Vleeschouwer,

Delouille

et al. 2023

JGR Space Physics

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We propose a fully automated system to detect, aggregate, and classify sunspot groups according to the McIntosh scheme using ground‐based white light (WL) observations from the USET facility located at the Royal Observatory of Belgium. The sunspot detection uses a Convolutional Neural Network (CNN), trained from segmentation maps obtained with an unsupervised method based on mathematical morphology and image thresholding. Given the sunspot mask, a mean‐shift algorithm is used to aggregate individual sunspots into sunspot groups. This algorithm accounts for the area of each sunspot as well as for prior knowledge regarding the shape of sunspot group. A sunspot group, defined by its bounding box and location on the Sun, is finally fed into a CNN multitask classifier. The latter predicts the three components Z, p, and c in the McIntosh classification scheme. The tasks are organized hierarchically to mimic the dependency of the second (p) and third (c) components on the first (Z). The resulting CNN‐based segmentation is more accurate than classical unsupervised methods, with an enhancement up to 16% of F1 score in detection of the smallest sunspots, and it is robust to the presence of clouds. The automated clustering method was able to separate groups with an accuracy of 80%, when compared to hand‐made USET sunspot group catalog. The CNN‐based sunspot classifier shows comparable performances to methods using continuum as well as magnetogram images recorded by instruments on space mission. We also show that an ensemble of classifiers allows differentiating reliable and potentially incorrect predictions.

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Section: Sunspot Segmentationmentioning

confidence: 99%

SunSCC: Segmenting, Grouping and Classifying Sunspots From Ground‐Based Observations Using Deep Learning

Sayez,

De Vleeschouwer,

Delouille

et al. 2023

JGR Space Physics

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“…The OGAUC solar image archive is huge (e.g., Lourenço et al, 2019; Table 1) and several studies have been carried out to develop automatic methods for recognizing and studying these images. Barata et al (2018) and Carvalho et al (2020) have achieved promising results with software tools to automatically detect and analyse chromospheric plages. This type of work highlights the importance and timeliness of the OGAUC solar data.…”

Section: The Solar Images Collectionmentioning

confidence: 99%

The importance of scientific data and historical heritage of the geophysical and astronomical observatory of coimbra university for the study of geophysical sciences

Gomes

Ramos

Figueiredo

et al. 2022

Geoscience Data Journal

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“…Until the present day, more than 50 000 spectroheliograms were acquired by the OGAUC spectroheliograph (Lourenço et al, 2019). Recently, software tools have been developed to automatically detect and analyse chromospheric plages (Barata et al, 2018) and sunspots (Carvalho et al, 2020) on solar images acquired at the OGAUC.…”

Section: Solar Observations At the Ogaucmentioning

confidence: 99%

Testing the Accuracy of Coimbra Astronomical Observatory Solar Filament Historical Series (1929–1941)

et al. 2021

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The present work aims to validate the positions of solar filaments published in the Annals of Coimbra University Astronomical Observatory, currently the Geophysical and Astronomical Observatory of the University of Coimbra, corresponding to years 1929 to 1941. The published Stonyhurst positions were obtained by an original method devised in the early 20th century that used a spherical calculator instrument, a wood-made model of the Sun. We used the digital images of the original spectroheliograms to measure the positions of the filaments, and heliographic coordinates were determined with the routines implemented in the Python package Sunpy. The correlation coefficients between both sets of coordinates are positive and highly significant. The results validate the method used at the Coimbra observatory and the published data. We conclude that the Coimbra solar filament catalogues are reliable and can therefore be considered for future solar activity studies.

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Comparison of automatic methods to detect sunspots in the Coimbra Observatory spectroheliograms

Cited by 14 publications

References 35 publications

SunSCC: Segmenting, Grouping and Classifying Sunspots From Ground‐Based Observations Using Deep Learning

SunSCC: Segmenting, Grouping and Classifying Sunspots From Ground‐Based Observations Using Deep Learning

The importance of scientific data and historical heritage of the geophysical and astronomical observatory of coimbra university for the study of geophysical sciences

Testing the Accuracy of Coimbra Astronomical Observatory Solar Filament Historical Series (1929–1941)

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