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

Author:

Fan Dongxin1,Yang Yunfei1ORCID,Feng Song1,Dai Wei1,Liang Bo1,Xiong Jianping2

Affiliation:

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

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.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Yunnan Key Research and Development Program

Yunnan Applied Basic Research Project

SDO

SOHO

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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