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Applied Sciences
Volume 14
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10.3390/app14010007
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Article

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

by
Dongxin Fan
1,
Yunfei Yang
1,*,
Song Feng
1,
Wei Dai
1,
Bo Liang
1 and
Jianping Xiong
2
1
Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming 650500, China
2
Yunnan Astronomical Observatories, Kunming 650051, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(1), 7; https://doi.org/10.3390/app14010007
Submission received: 12 November 2023 / Revised: 8 December 2023 / Accepted: 12 December 2023 / Published: 19 December 2023
(This article belongs to the Special Issue Advanced Image Analysis and Processing Technologies and Applications)
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Abstract

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.
Keywords: sunspots; multiscale; ultra-small; SIPNet sunspots; multiscale; ultra-small; SIPNet

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MDPI and ACS Style

Fan, D.; Yang, Y.; Feng, S.; Dai, W.; Liang, B.; Xiong, J. SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images. Appl. Sci. 2024, 14, 7. https://doi.org/10.3390/app14010007

AMA Style

Fan D, Yang Y, Feng S, Dai W, Liang B, Xiong J. SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images. Applied Sciences. 2024; 14(1):7. https://doi.org/10.3390/app14010007

Chicago/Turabian Style

Fan, Dongxin, Yunfei Yang, Song Feng, Wei Dai, Bo Liang, and Jianping Xiong. 2024. "SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images" Applied Sciences 14, no. 1: 7. https://doi.org/10.3390/app14010007

APA Style

Fan, D., Yang, Y., Feng, S., Dai, W., Liang, B., & Xiong, J. (2024). SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images. Applied Sciences, 14(1), 7. https://doi.org/10.3390/app14010007

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