Galaxy Image Classification Based on Citizen Science Data: A Comparative Study
Galaxy Image Classification Based on Citizen Science Data: A Comparative Study
复制标题
基于公民科学数据的星系图像分类：比较研究
作者：
Manuel Jiménez;Mercedes Torres Torres;R. John;I. Triguero
许多研究领域现在都面临着由专业设备自动生成的大量数据。天文学是一门处理大量图像的学科，这些图像很难单独由专家处理。因此，天文学家一直依赖于群众的力量，作为一种公民科学的形式，由业余爱好者对星系图像进行分类。然而，新一代望远镜将以更高的速度产生图像，这凸显了这种方法的局限性，使用机器学习方法进行自动分类被认为是必不可少的。本文的目标是揭示星系图像的自动分类，探索两种不同的机器学习策略。首先，在经典的方法，包括特征提取与分类器，我们比较了国家的最先进的特征提取器这个问题，WND-CHARM，与我们的建议基于自动编码器的星系图像的特征提取。然后，我们将这些结果与使用卷积神经网络的端到端分类进行比较。为了更好地利用可用的公民科学数据，我们还研究了一个利用业余和专家标记数据的预训练方案。我们的实验表明，与WND-CHARM相比，自动编码器大大加快了特征提取的速度，两种分类策略，无论是使用卷积神经网络还是特征提取，都达到了相当的准确性。然而，在卷积神经网络中使用预训练使我们能够提供更好的结果。
Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers have been relying on the power of the crowds, as a form of citizen science, for the classification of galaxy images by amateur people. However, the new generation of telescopes that will produce images at a higher rate highlights the limitations of this approach, and the use of machine learning methods for automatic classification is considered essential. The goal of this paper is to shed light on the automated classification of galaxy images exploring two distinct machine learning strategies. First, following the classical approach consisting of feature extraction together with a classifier, we compare the state-of-the-art feature extractor for this problem, the WND-CHARM, with our proposal based on autoencoders for feature extraction on galaxy images. We then compare these results with an end-to-end classification using convolutional neural networks. To better leverage the available citizen science data, we also investigate a pre-training scheme that exploits both amateur- and expert-labelled data. Our experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy. The use of pre-training in convolutional neural networks, however, has allowed us to provide even better results.
公民科学前沿：人机系统的效率、参与度和偶然发现
DOI：
10.1073/pnas.1807190116
发表时间：
2019
期刊：
Proceedings of the National Academy of Sciences
影响因子：
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作者：
Trouille, Laura;Lintott, Chris J.;Fortson, Lucy F.
通讯作者：
Fortson, Lucy F.
DOI：
10.1109/iccv.2019.00656
发表时间：
2019-10
期刊：
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
影响因子：
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作者：
Fariborz Taherkhani;Hadi Kazemi;Ali Dabouei;J. Dawson;N. Nasrabadi
通讯作者：
Fariborz Taherkhani;Hadi Kazemi;Ali Dabouei;J. Dawson;N. Nasrabadi