"A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission" [article][pdf][media]

Azim Ahmadzadeh, Dustin J. Kempton, and Rafal A. Angryk

Can we classify solar events solely based on a set of simple image parameters? In other words, how good a supervised model can learn to distinguish an Active Region (AR) from a Coronal Hole (CH) solar event, by only looking at the pixel colors and the regional textures?

The contribution of this study is as follows:

• Analysis of each of the ten image parameters and determining their tuning variables (if any),

• Tuning each of the ten image parameters, using F test,

• Utilizing supervised classifiers for detection of AR and CH events based on the feature space of the tuned image parameters,

• Comparing the extracted features from JP2 images versus FITS files, in terms of the classification performance, and

• Providing a public API to access the dataset of 6+ years of the extracted features on SDO image, with the cadence of 6 minutes.

We conduct a thorough investigation on performance of a set of ten image parameters on AIA solar images captured by the Solar Dynamics Observatory mission and present our dataset in the form of a publicly available online interface. We target the two solar events, namely active region and coronal hole, which are in particular of interest for classification and prediction of solar flares. Since the AIA’s four telescopes provide narrow-band imaging of seven extreme ultraviolet band passes and two ultraviolet channels, we carefully tune the image parameters one by one to achieve a higher performance on each of those channels, in classification of active regions and coronal holes. In addition to taking into account the differences between the channels, we perform our experiments on two different image formats, FITS and JP2. The FITS format is used to store the original AIA’s images after digitization, and the JP2 format is utilized by Helioviewer for reducing the size of their repository and making processing of such large datasets more feasible. The AIA images in JP2 format, similar to those in FITS format, are 4k images, but they are approximately 5 to 14 times smaller in size. This makes the computation time of any algorithm significantly faster on JP2 images. Our results of such comparison show that for our purpose, by using the JP2 format we can obtain the same level of classification accuracy for all of our image parameters, that we could achieve wiht the FITS images.

• HEK: This is the source of the spatiotemporal data used in this study. The HEK system, as a centralized archive of solar event reports, is populated with the events detected by its Event Detection System (EDS) from SDO data. There are considered 18 different classes of events such as active region, coronal hole, and flare. For each event class, a unique set of required and optional attributes is defined. Each event must have a duration and a bounding box that contains the event in space and time. We use this information to map the meta data of the reported events to the corresponding AIA images. Among the existing classes, we are only interested in active region and coronal hole events for this study. Active regions are reported and assigned numbers daily by the Space Weather Prediction Center (SWPC) of NOAA (National Oceanic and Atmospheric Administration). Each active region observation, as Hurlburt et al. (2010) explain, is an event bounded within a 24-hour time interval. Therefore, HEK considers all NOAA active regions with the same active region number to be the same active region.

• AIA: This is the source of the image data for our study. The AIA’s four telescopes provide narrow-band imaging of seven extreme ultraviolet (EUV) band passes (94–Å, 131–Å, 171–Å, 193–Å, 211–Å, 304–Å, and 335–Å) and two UV channels (1600–Å and 1700–Å). The captured 4k images of the Sun, which are full-disk snapshots with the cadence of 12 seconds, are compressed on board and without being recorded on orbit, are transmitted to SDO ground stations. The received raw data (Level 0) are archived on magnetic tapes in JSOC science-data processing facility. The uncompressed data is then exported as FITS files with the data represented as 32-bit floating values. At this point, images are already calibrated, however, some corrections and cleaning are still required due to the existence of a small residual roll angle between the four AIA telescopes. At this stage (Level 1.5), the data is ready for analysis. In some repositories including Helioviewer, the FITS files are converted to JP2 format to reduce the volume of their database. In this study, we use the level 1.5 FITS files and we refer to them as the “rawFITS”, as opposed to the “clipped FITS”.

As mentioned above, SDO image data is available in both JP2 and FITS formats. FITS, short for Flexible Image Transport System, is a data format for recording digital images of scientific observations. This format was proposed as a solution to the data transport problem. I studied the distribution of pixel intensities of FITS files here. For processing of the FITS files we use the nom-tam-fits Java library.

For each parameter, first, we find the set of n key constraints (or variables), and identify appropriate numeric domains, d_i, for each constraint i ∈{1, 2, ..., n}. As a result, we will have a feature space of dimension |d_1| × |d_2| × ··· × |d_n|, for that particular image parameter, where |d_i| is the cardinality of the domain set d_i. In addition, to describe a particular event, a region of interest must be processed that spans over a variable number of grid cells. The image parameters will extract features from this region. If the region spans over k grid cells, it will then be represented by a vector of length k, for each image parameter. To be able to compare different-length vectors, we use the seven-number summary on the resultant vectors. This would map each feature computed on a region to seven different values. This multiplies the dimensionality of our space by 7. Besides, since the AIA images are captured in 9 ultraviolet (UV) and extreme ultraviolet (EUV) wavelength channels that produce significantly different images of the Sun, a higher-level tuning

is expected to take such differences into consideration and look for the best setting per wavelength. This additional layer multiplies the dimension of the feature space by 9. Therefore, for each of the feature spaces defined by an image parameter that has n different variables, the final dimension will be equal to |d1| × |d2| × ··· × |dn| × 9 × 7.

Our methodology can be summarized in the following five steps:

1. Determining the dimension of the feature space (i.e., identifying the constraints and their domains),

2. Building the feature space for the period of one month (i.e., January 2012),

3. Reducing the dimensionality of the feature space using F-test (i.e., finding the best settings per wavelengths),

4. Building the (reduced) feature space for the period of one year (i.e., 2012),

5. Measuring the quality of the parameter using supervised learning.

For the learning and prediction phase, we employed the same methodology in collection of data that was used by Schuh et al. (2017) to collect one year worth of AIA images over the entire 2012 calendar year and the spatiotemporal data related to the solar events reported in this period. Here, we only briefly explain the data acquisition process and refer the interested reader to the article where the entire process is explained in great detail.

We target two solar event types, namely active region (AR) and coronal hole (CH), which are in particular of interest for heliophysicists and also because of their similar reporting characteristics that make region identification easier. In year 2012, HEK reported 13,518 AR and 10,780 CH event instances, at approximately a four hour cadence. Since there are more AR instances, we first collect all of those instances and then we look for CH instances within a time window of ±60 minutes from each report of an AR instance. Those AR instances that could not be paired with a temporally close CH instance are dropped. The report of each event contains both temporal and spatial information. We use the time stamps of the reports to retrieve the corresponding AIA images (in JP2 and FITS format). The spatial data of each instance consists of a center point for the reported event, its bounding box, and polygonal outline. We use the bounding boxes to extract the image parameters on the region corresponding to each event instance in our training and test phase. With such constraints, we managed to retrieve 2,116 unique pairs of AR and CH instances. As our supervised learning model requires a control class, an event type that points to a region of solar disk with no report of any other solar events, an artificial event called quiet sun (QS) is introduced. To collect a set of such instances temporally linked to our AR-CH collection, for each report of an AR event, the bounding box of that event is used to randomly search for regions that have no intersection with any reports of AR or CH events.

The result of the above classifications, for the four image parameters that we have tuned, is shown below.