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首页> 外文期刊>The Astrophysical Journal. Supplement Series >A Curated Image Parameter Data Set from the Solar Dynamics Observatory Mission
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A Curated Image Parameter Data Set from the Solar Dynamics Observatory Mission

机译：来自太阳能动力学天文台任务的策划图像参数数据

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摘要

We provide a large image parameter data set extracted from the Solar Dynamics Observatory (SDO) mission's Atmospheric Imaging Assembly (AIA) instrument, for the period of 2011 January through the current date, with the cadence of 6 minutes, for nine wavelength channels. The volume of the data set for each year is just short of 1 TiB. Toward achieving better results in the region classification of active regions and coronal holes, we improve on the performance of a set of 10 image parameters, through an in-depth evaluation of various assumptions that are necessary for calculation of these image parameters. Then, where possible, a method for finding an appropriate setting for the parameter calculations was devised, as well as a validation task to show our improved results. In addition, we include comparisons of JP2 and FITS image formats using supervised classification models, by tuning the parameters specific to the format of the images from which they are extracted and specific to each wavelength. The results of these comparisons show that utilizing JP2 images, which are significantly smaller files, is not detrimental to the region classification task that these parameters were originally intended for. Finally, we compute the tuned parameters on the AIA images and provide a public API (see http://dmlab.cs.gsu.edu/dmlabapi/) to access the data set. This data set can be used in a range of studies on AIA images, such as content-based image retrieval or tracking of solar events, where dimensionality reduction on the images is necessary for feasibility of the tasks.
机译：我们提供从太阳能动力学天文台（SDO）Mission的大气成像组件（AIA）仪器中提取的大型图像参数数据集，2011年1月通过当前日期，九个波长通道的节奏为6分钟。每年的数据集的数量短于1个TIB。在实现有源区和冠状孔的区域分类中实现更好的结果，我们通过对计算这些图像参数所需的各种假设的深入评估来改善一组10个图像参数的性能。然后，在可能的情况下，设计了用于查找参数计算的适当设置的方法，以及验证任务以显示我们改进的结果。此外，我们包括JP2的比较和使用监督分类模型来使用监督分类模型的比较。通过调整特定于它们的图像的格式和特定​​于每个波长的图像的格式的参数进行调整。这些比较的结果表明，利用具有明显较小文件的JP2图像，对这些参数最初用于的区域分类任务不利。最后，我们在AIA图像上计算调谐参数并提供公共API（请参阅http://dmlab.cs.gsu.edu/dmlabapi/）以访问数据集。该数据集可以用于AIA图像的一系列研究，例如基于内容的图像检索或太阳能事件的跟踪，其中对图像的可行性是必要的，用于图像的维度降低是必要的。

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