Automatic detection of sunspots from solar images using fractional-order derivatives and extraction of their attributes

Madhan, Veeramani; Sudhakar, M.S.

doi:10.1016/j.asr.2023.08.053

Cited by 1 publication

(2 citation statements)

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“…Table 1 lists the number of manually counted and detected sunspots with different scales in each region of Figure 5. According to the results proposed by Tlatov [1,7], we divided the sunspots into six groups according to their sizes: (0, 4] msh, (4, 10] msh, (10,20] msh, (20, 100] msh, (100, 200] msh, and (200, +∞] msh. The large sunspots are in the minority, while the ultra-small sunspots form the majority.…”

Section: Instancesmentioning

confidence: 99%

“…Some assistive technologies were adopted to set thresholds, such as the statistical Bayesian method [16], fuzzy-sets [17], and the simulated annealing genetic method [18]. Recently, an adaptive method was adopted to process images under various conditions, while processing multiple images taken within a short time to eliminate the seeing effect [19], and a localized sunspot detection scheme was proposed using the fractional-order derivative mask [20]. The threshold is very critical for sunspot detection, in which a larger threshold could miss some of the pixels that are part of the sunspots while a smaller threshold could increase noise.…”

Section: Introductionmentioning

confidence: 99%

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SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images

Fan,

Yang,

Feng

et al. 2023

Applied Sciences

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Photospheric magnetic fields are manifested as sunspots, which cover various sizes over high-resolution, full-disk, solar continuum images. This paper proposes a novel deep learning method named SIPNet, which is designed to extract and segment multiscale sunspots. It presents a new Switchable Atrous Spatial Pyramid Pooling (SASPP) module based on ASPP, employs an IoU-aware dense object detector, and incorporates a prototype mask generation technique. Furthermore, an open-source framework known as Slicing Aided Hyper Inference (SAHI) is integrated on top of the trained SIPNet model. A comprehensive sunspot dataset is built, containing more than 27,000 sunspots. The precision, recall, and average precision metrics of the SIPNet & SAHI method were measured as 95.7%, 90.2%, and 96.1%, respectively. The results indicate that the SIPNet & SAHI method has good performance in detecting and segmenting large-scale sunspots, particularly in small and ultra-small sunspots. The method also provides a new solution for solving similar problems.

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