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
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
2 articles.
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