A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission

TL;DR

This paper introduces a large, curated dataset of solar images from the SDO mission, along with improved image parameters and a public API, facilitating advanced solar image analysis and classification tasks.

Contribution

It provides a comprehensive, validated dataset of AIA images with optimized parameters and a comparison of image formats, enhancing solar region classification research.

Findings

JP2 images are effective for classification despite smaller size

Optimized parameters improve active region and coronal hole classification

Public API enables broad access and application of the dataset

Abstract

We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels. The volume of the dataset for each year is just short of 1 TiB. Towards achieving better results in the region classification of active regions and coronal holes, we improve upon the performance of a set of ten 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 settings 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 (http://dmlab.cs.gsu.edu/dmlabapi) to access the dataset. This dataset 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.

Full text

An example of a Research Note of the American Astronomical Society (RNAAS)

Ethan Vishniac

Editor-in-Chief

Johns Hopkins University

Chris Lintott

RNAAS Editor

Oxford University

Greg J. Schwarz

American Astronomical Society

2000 Florida Ave., NW, Suite 300

Washington, DC 20009-1231, USA

August Muench

American Astronomical Society

2000 Florida Ave., NW, Suite 300

Washington, DC 20009-1231, USA

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