神经网络在错误太阳图像识别中的应用

IF 2.4 3区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
Kiran Jain, Mitchell Creelman
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引用次数: 0

摘要

一个多世纪以来,人们在不同的光谱范围内拍摄了太阳图像。最初,这些图像是在照相板上拍摄的,随着CCD相机的发展,图像从模拟格式过渡到数字格式。分析数字图像使我们能够更有效地识别和分析太阳表面的趋势和特征。然而,由于仪器故障或环境因素引起的并发症可能导致图像不理想。传统上,使用几个统计参数来检查图像质量,但这些措施并不总是产生令人满意的结果。在本文中,我们描述了一种卷积分类神经网络,用于近实时的龚氏多普勒图图像质量评估。我们还提供了一个案例研究,其中该方法显着提高了自动数据减少管道中科学数据产品的质量,而无需任何人为干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of a Neural Network for Identifying Erroneous Solar Images

For over a century, solar images have been captured across different spectral ranges. Initially, these images were taken on photographic plates, and with the development of CCD cameras, the images transitioned from analogue to digital formats. Analyzing digital images enables us to identify and analyze trends and features on the solar disk more efficiently. However, complications due to instrument malfunction or environmental factors can result in suboptimal images. Traditionally, several statistical parameters are used to check image quality, but these measures do not always yield satisfactory results. In this article, we describe a convolutional classification neural network for near-real time image quality assessment of GONG Dopplergrams. We also present a case study where this approach significantly improved the quality of science data products in an automated data reduction pipeline without any human intervention.

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来源期刊
Solar Physics
Solar Physics 地学天文-天文与天体物理
CiteScore
5.10
自引率
17.90%
发文量
146
审稿时长
1 months
期刊介绍: Solar Physics was founded in 1967 and is the principal journal for the publication of the results of fundamental research on the Sun. The journal treats all aspects of solar physics, ranging from the internal structure of the Sun and its evolution to the outer corona and solar wind in interplanetary space. Papers on solar-terrestrial physics and on stellar research are also published when their results have a direct bearing on our understanding of the Sun.
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