一种层次化的太阳黑子快速自动识别方法

赵梓良,刘家真,胡真,等. 一种层次化的太阳黑子快速自动识别方法[J]. 光电工程,2020,47(7):190342. doi: 10.12086/oee.2020.190342
引用本文: 赵梓良,刘家真,胡真,等. 一种层次化的太阳黑子快速自动识别方法[J]. 光电工程,2020,47(7):190342. doi: 10.12086/oee.2020.190342
Zhao Z L, Liu J Z, Hu Z, et al. A hierarchical method for quick and automatic recognition of sunspots[J]. Opto-Electron Eng, 2020, 47(7): 190342. doi: 10.12086/oee.2020.190342
Citation: Zhao Z L, Liu J Z, Hu Z, et al. A hierarchical method for quick and automatic recognition of sunspots[J]. Opto-Electron Eng, 2020, 47(7): 190342. doi: 10.12086/oee.2020.190342

一种层次化的太阳黑子快速自动识别方法

  • 基金项目:
    国家自然科学基金资助项目(11727805,11733005);中国科学院光电技术研究所大学生创新实践训练计划(20184001188)
详细信息
    作者简介:
    通讯作者: 刘洋毅(1989-),男,博士,助理研究员,主要从事太阳高分辨力成像技术的研究。E-mail:liuyangyi_ioe@163.com
  • 中图分类号: TP391

A hierarchical method for quick and automatic recognition of sunspots

  • Fund Project: Supported by National Natural Science Foundation of China (11727805 and 11733005) and College Students' Innovation Practice Training Program of Institute of Optics and Electronics, Chinese Academy of Sciences (20184001188)
More Information
  • 太阳黑子的观测与识别是太阳物理学的重要任务。通过对太阳黑子的观测与分析,太阳物理学者可以更准确地分析以及预测太阳活动。随着观测仪器的不断进步,太阳全日面图像数据量也在快速增长。为了快速、准确地进行太阳黑子的自动识别和标注,本文提出了一种两层的太阳黑子识别模型。第一层模型采用深度学习模型YOLO,并使用基于交并比的k均值算法优化YOLO的参数,最终的YOLO模型能够识别绝大多数较大黑子和黑子群,仅有极少数孤立的本影较小的黑子未能识别。为进一步提高这类小黑子的识别率,第二层模型采用AGAST特征检测算法专门识别遗漏的小黑子。在SDO/HMI太阳黑子数据集上的实验结果表明,应用本文的层次化模型,各种形态的太阳黑子均能被有效识别,且识别速率高,从而能够实现实时太阳黑子检测任务。

  • Overview: Sunspots are small dark spots, patches or regions appearing on the sun's surface where strong magnetic fields converge. As an important solar phenomenon, observation and analysis of sunspots can promote understanding and learning of solar activities. For example, it can help astrologists to study the relevance of sunspots groups with flare eruptions. With the development of solar physics and observation instrument, methods of sunspots detection proposed earlier can not satisfy the rapid growth of data amount and data processing performance. Thus, it is urgent for solar physics to propose new methods to detect sunspots with higher accuracy and efficiency.

    Traditional digital image processing methods and algorithms based on the sliding window are usually characterized by a slow speed and cannot achieve high accuracy. For instance, pure digital image processing methods are usually not flexible enough to detect sunspots because of the divergence of colors and patterns of different types of sunspots. In addition, the sliding window algorithm proposed earlier is of high time complexity, which shows poor performance in practical applications. To solve problems mentioned above, this paper aims to rapidly recognize all types of sunspots for real-time detection.

    We proposed a hierarchical model composed of two components. The first layer is based on deep learning model YOLO. According to the nature of YOLO network, the raw image data with a size of 4096 pixels would be divided into smaller sub-images because sunspots are relatively tiny compared with the whole solar image. At the same time, sunspots will be compressed and disappear in neural network, which will influence the training process of network. In order to improve the ability of first layer to detect more smaller sunspots, the k-means algorithm is applied to optimize the anchor parameter in YOLO model. After the first layer model, most sunspots and sunspot groups are able to be recognized, with just a few smaller sunspots being unidentified. For the purpose of detecting more unidentified smaller sunspots in the first layer, the second layer utilizes AGAST algorithms for feature detection, in which the smaller sunspots are viewed as speckles.

    In the experiment, 700 original images from SDO/HMI data set are used to train YOLO network. After training process, the loss function reduces from 935.12 to 0.22. The detecting results show that all kinds of sunspots can be recognized effectively with intersection-over-union being 73.41%, detecting accuracy being about 98.50%, and error recognition rate being around 0.60%. Therefore, the hierarchical model can be used to complete real-time sunspot detection task, and relevant ideas and models could also be applied to solve other object detection problems.

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  • 图 1  层次化的太阳黑子识别模型示意图

    Figure 1.  Sketch of hierarchical sunspot detecting model

    图 2  SDO/HMI各类型太阳全日面图

    Figure 2.  All types of solar full-disk images in SDO/HMI dataset

    图 3  数据预处理流程

    Figure 3.  Process of data pretreatment

    图 4  第一层模型:基于YOLO的深度神经网络

    Figure 4.  The first layer: deep neural network based on YOLO

    图 5  FAST算法的基本原理示意图

    Figure 5.  Sketch map of the basic principle of FAST algorithm

    图 6  损失函数的部分变化过程

    Figure 6.  The variation process of of loss function

    图 7  太阳黑子识别结果

    Figure 7.  Results of sunspot detection

    图 8  测试交并比的分布

    Figure 8.  Distribution of intersection-over-union in test dataset

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出版历程
收稿日期:  2019-06-21
修回日期:  2019-11-04
刊出日期:  2020-07-01

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