Sunspots Extraction in PMO Sunspot Drawings Based on Deep Learning

Abstract

Sunspot numbers and sunspot areas are the most fundamental indices of long-term solar activity levels and the solar magnetic dynamo. This paper presents a deep-learning method for segmenting the components of sunspots in the Purple Mountain Astronomical Observatory (PMO) historical hand drawings spanning from 1954 to 2011. A total of 44568 samples were labeled as the following four types to build the training set and the test set at a ratio of 9:1. They are (1) pores without penumbrae, (2) spots with penumbrae, (3) umbrae within spots, and (4) holes within spots. A Hybrid Task Cascade Region-based Convolutional Neural Networks (HTC RCNN) is adopted; it is designed as three cascade stages adapted to increasingly higher Intersection over Unions to obtain increasing detector quality. The features of sunspots are extracted and fused by a backbone combining residual network 50 and a feature pyramid network. After training the network using the training set, the method is tested by the test set. The precision, recall, and mean Average Precision are 0.90, 0.90, and 0.89, respectively, indicating that the method has a satisfying performance. The components of each sunspot are extracted separately, yielding the numbers and areas of pores, spots, umbrae and penumbrae. Detailed data of the PMO drawings from 1954 to 2011 are shared in public (http://61.166.157.71/HTCSD.html). It is another piece of the puzzle of long-term solar activity cycles and solar magnetic dynamo research.

Sunspot extraction and hemispheric statistics of YNAO sunspot drawings using deep learning

“…We made a careful comparison among the segmentation results of HTC method (Xu et al, 2021), CondInst method (Tian et al, 2020), and SPR-Mask method. The HTC method was used for segmenting sunspots in PMO drawings, which is based on a hybrid task cascade (HTC) model.…”

“…Deep learning methods need data set including training set and testing set. Fortunately, owing to similarity of the sunspot drawing styles of YNAO and the Purple Mountain Astronomical Observatory (PMO), the data set built by Xu et al (2021) were adopted directly without any change in this work. They labeled a total of 39 055 and 5 513 samples as the training set and the test set, separately.…”

“…To focus on the drawn sunspots within the solar disk, the solar disk of each drawing was extracted by the method used by Xu et al (2021). In brief, the reprinted information of the papers was extracted using a method based on a RGB threshold (Otsu, 1978).…”

“…The Chinese sunspot database is shared at http://Sun.bao.ac.cn/SHDA data/ (Lin et al, 2019), which the data were extracted from the handwritten characters recorded in the drawings using a handwritten character recognition method (Zheng et al, 2016). Xu et al (2021) proposed a deep learning method to classify and segment the drawn sunspots recorded in the Purple Mountain Astronomical Observatory (PMO), however, they presented that the segmentation edges of sunspots need to be improved.…”

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

“…In recent years, some deep learning methods have been used in the field of sunspots, for example, sunspot extraction from Chinese sunspot drawings [21,22]. Chola [23] adopted AlexNet for classifying sun images into an active sun or quiet sun.…”