Identification of Sunspots on SODISM Full-Disk Solar Images

Alasta, Amro F.; Algamudi, Abdulrazag; Qahwaji, Rami; Ipson, Stanley S.

doi:10.6025/jmpt/2018/9/3/102-112

“…This operation decomposes the original image into four lower resolution sub-bands, referred to as low-low (LL), low-high (LH), high-low (HL) and high-high (HH) as shown in Figure 2. The latter three sub-bands occupy the upper frequency spectrum of the original image and the resolution enhancement technique is applied here in this application [13]. Wavelet-based methods do this by enhancing image resolution through estimating the high frequency information from the given image.…”

Section: Literature Surveymentioning

confidence: 99%

Filling Factors of Sunspots in SODISM Images

Alasta¹,

Algamudi²,

Almesrati³

et al. 2019

AETiC

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1

0

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The calculated filling factors (FFs) for a feature reflect the fraction of the solar disc covered by that feature, and the assignment of reference synthetic spectra. In this paper, the FFs, specified as a function of radial position on the solar disc, are computed for each image in a tabular form. The filling factor (FF) is an important parameter and is defined as the fraction of area in a pixel covered with the magnetic field, whereas the rest of the area in the pixel is field-free. However, this does not provide extensive information about the experiments conducted on tens or hundreds of such images. This is the first time that filling factors for SODISM images have been catalogued in tabular formation. This paper presents a new method that provides the means to detect sunspots on full-disk solar images recorded by the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The method is a totally automated detection process that achieves a sunspot recognition rate of 97.6%. The number of sunspots detected by this method strongly agrees with the NOAA catalogue. The sunspot areas calculated by this method have a 99% correlation with SOHO over the same period, and thus help to calculate the filling factor for wavelength (W.L.) 607nm.

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Detection and classification of sunspots via deep convolutional neural network

Chola

¹

,

Benifa

²

2022

Global Transitions Proceedings

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Filling Factors of Sunspots in SODISM Images

Alasta

¹

,

Algamudi²,

Almesrati³

et al. 2019

SSRN Journal

Self Cite

0

View full text Add to dashboard Cite

The calculated filling factors (FFs) for a feature reflect the fraction of the solar disc covered by that feature, and the assignment of reference synthetic spectra. In this paper, the FFs, specified as a function of radial position on the solar disc, are computed for each image in a tabular form. The filling factor (FF) is an important parameter and is defined as the fraction of area in a pixel covered with the magnetic field, whereas the rest of the area in the pixel is field-free. However, this does not provide extensive information about the experiments conducted on tens or hundreds of such images. This is the first time that filling factors for SODISM images have been catalogued in tabular formation. This paper presents a new method that provides the means to detect sunspots on full-disk solar images recorded by the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The method is a totally automated detection process that achieves a sunspot recognition rate of 97.6%. The number of sunspots detected by this method strongly agrees with the NOAA catalogue. The sunspot areas calculated by this method have a 99% correlation with SOHO over the same period, and thus help to calculate the filling factor for wavelength (W.L.) 607nm.

show abstract

Identification of Sunspots on SODISM Full-Disk Solar Images

Cited by 3 publications

References 9 publications

Filling Factors of Sunspots in SODISM Images

Filling Factors of Sunspots in SODISM Images

Detection and classification of sunspots via deep convolutional neural network

Filling Factors of Sunspots in SODISM Images

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