Classification of Planetary Nebulae through Deep Transfer Learning
Classification of Planetary Nebulae through Deep Transfer Learning
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DOI：
10.3390/galaxies8040088
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发表时间：
2020-12
期刊：
影响因子：
2.5
通讯作者：
Dayang N. F. Awang Iskandar;A. Zijlstra;I. McDonald;R. Abdullah;G. Fuller;A. Fauzi;Johari Abdullah
中科院分区：
文献类型：
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作者：
Dayang N. F. Awang Iskandar;A. Zijlstra;I. McDonald;R. Abdullah;G. Fuller;A. Fauzi;Johari Abdullah
这项研究调查了使用深度学习（DL）对行星状星云（PNe）进行分类的有效性。它侧重于区分PNE与其他类型的对象，以及它们的形态分类。我们采用了使用三种ImageNet预训练算法的深度迁移学习方法。是次研究使用了香港/澳大利亚天文台/斯特拉斯堡天文台H阿尔法行星星云研究平台数据库（HASH DB）及全景巡天望远镜及快速反应系统（Pan-STARRS）的图像。我们发现，即使没有任何参数调整，该算法在区分True PNe与其他类型的对象方面也取得了很大的成功。马修斯相关系数为0.9。我们的分析表明，DenseNet 201是最有效的DL算法。对于形态分类，我们发现对于三个类别，双极，椭圆和圆形，一半的对象被正确分类。进一步的改进可能需要更多的数据和/或培训。我们讨论了未来工作的权衡和潜在途径，并得出结论，深度迁移学习可用于对宽视场天文图像进行分类。
This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.