the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Auroral alert version 1.0: Two-step automatic detection of sudden auroral intensification from all-sky jpeg images
- Swedish Institute of Space Physics, Bengt Hultqvist vägen 1, Box 812, S-98128 Kiruna, Sweden
- Swedish Institute of Space Physics, Bengt Hultqvist vägen 1, Box 812, S-98128 Kiruna, Sweden
Abstract. A real-time alert system of sudden and significant intensification of auroral arc with expanding motion (we call it "Local-Arc-Breaking" hereafter) was developed for Kiruna all-sky camera (ASC) using ASC jpeg images. The identification is made in two steps: (1) Using an "expert system" in which a combinations of simple criteria is applied to each pixels with calculations afterward (expert system), each jpeg image of the ASC is converted into a simple set of numbers, or "ASC auroral index", representing the occupancy of auroral pixels and characteristic intensity of the brightest aurora in the image. (2) Using this ASC auroral index, the level of auroral activity is estimated, aiming Level 6 as clear Local-Arc-Breaking and Level 4 as precursor for it (reserving Levels 1–3 for less active aurorae).
The first step is further divided into two stages: (1a) Using simple criteria for R (red), G (green), B (blue), and H (hue) values in the RGB and HLS colour codes, each pixel of a jpeg image is classified into several categories according to its colour as "visible diffuse", "green arc", "strong aurora" (which means saturated or mixed with N2 red line at 670 nm), "cloud", "artificial light", and "moon". (1b) The percentage of the occupying area (pixel coverage) for each category and the characteristic intensity of "strong aurora" are calculated.
The obtained ASC aurora index is posted in both a ascii format and plots on a real-time bases at https://www.irf.se/alis/allsky/nowcast/. When Level 6 is detected, automatic alert E-mail is sent out to the registered addresses immediately. The alert system started 5 November, 2021, and the results (both Level 6 detection and Level 4 detection) were compared to the manual (eye-)identification of the auroral activity during the rest of the auroral season of Kiruna ASC (i.e., total five months until April 2022). Unless the Moon or cloud blocks the brightened region, nearly one-to-one correspondence between Level 6 and Local-Arc-Breaking judged by original ASC images is achieved within ten minutes uncertainty.
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Masatoshi Yamauchi and Urban Brändstöm
Status: open (until 30 Jul 2022)
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RC1: 'Comment on egusphere-2022-331', Anonymous Referee #1, 16 Jun 2022
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The submitted Manuscript presents an approach for Aurora real-time detection based on hemispheric RGB camera images. In contrast to other approaches using deep learning, the introduced method uses spectra indices calculated from RGB image data.
Unfortunately, the authors assume exhaustive preknowledge on the detection / measurement of aurora that impedes an easy and exciting entry into the topic.
The method is very complex and hard to understand due to missing figures that would definetely help to grasp the matter. Furthermore, it sounds like the approach is only applicable to one study area using one specific camera setup where the authors developed the method. It was not stated that the approch was tested / validated at some else location or using some other camera configuration.
The evaluation is based on manual observations that AFAIK could be very subjective. Unfortunately it is not stated if the reference data is based on observations of several operateurs to have some quality control on the Groud Truth data.Due to the mentioned points I recommend to resubmit the paper after extensive rework as the method itself sounds interesting.
Some more comments are given directly in the attaced PDF.
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AC1: 'Reply on RC1 (our plan toward revision)', Masatoshi Yamauchi, 27 Jun 2022
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Thank you for your encouraging comments and for reminding that potential readers are much wider than the auroral observation community.
To cover wider readers, we will add three (or more) figures in the revision: keogram, ASC image with different category and area marked, and image showing N2 red line) with more explanation in text. We actually have them nearly ready (used in oral presentations for non-auroral community) but not included here (this is our mistake). We also plan to add a table of example of actual classification (to be used in combination with the new figure).
In the planed revision, we will also explain the speciality of aurora where no classification method (including machine learning) can be applied to different cameras without changing parameters or more fundamental selection (for machine learning method, training set should be different between different cameras, and definition of such training set is as subjective as the colour definition in the present method).
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AC1: 'Reply on RC1 (our plan toward revision)', Masatoshi Yamauchi, 27 Jun 2022
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CC1: 'Comment on egusphere-2022-331', Christian Kehl, 28 Jun 2022
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I thank the authors for the interesting manuscript. The manuscripts details a simple method to classify images in an ASC aurora image collection according to the presented ASC index. The method is computationally lightweight, which is important for the goal of nowcasting local arc breaking of auroras in all-sky camera images. The single ASC index indicator makes the classification outcome easily interpretable by experts and novices in the field.
That said, next to the line-by-line review attached, there are some general comments that need addressing in a potential revision.
- The manuscript makes the data appear as very complex, multi-dimensional and challenging. Sadly, this only appears so due to ambiguous writing. In the end, after careful review, it appears the images are rather small (150,00 pixel) 3-band images, which is hidden behind confusing writing and misleading illustrations (fig. 1). This needs fixing.
- The manuscript makes the developed method appear as intricate-yet-powerful, which is related to how the data is presented. In the end, the presented method is a multi-level, double-bound thresholding with 5 different thresholding levels (i.e. categroies). This method is indeed simple, but introduces a considerable amount of fine-tuned parameters due to the lack of in-depth prior image processing. This also makes the formulation ambiguous and the method very difficult to replicate for other observatories.
- The manuscript requires more context information to make it accessible and comprehensible to researchers outside the expert aurora observation community. Special terms and phenomena such as the N2 red line or local arc breaking are neitehr explained in-text or referenced in literature. It is very hard to understand the message and research objectives of the manuscript for an common EGU audience. There is still more than sufficient space available for some added references to give the reader information on where to find further details on the assumption baseline of the manuscript.
- The manuscript lacks references to make some context information understandable and to to make certain design decisions of the method easier to reason and comprehend.
- The manuscript lacks a details discussion of the imaging parameters (camera resolution, quantisation, etc.). The authors use JPEG images with small resolutions, dynamic exposure times from the camera, hence automatic white balancing, and an ingrained 3-band 8-bit pixel quantisation. The paper lacks any in-depth discussion of the actual influence of those instrumentation parameters. The paper lacks any comparison and impact assessment of the image compression error from JPEG. Overall, the manuscript treats the actual imaging influences superficially.
- The manuscript lacks a comparison with alternative, more up-to-date methods of image pattern recognition.
- The manuscript needs considerable language revision, as minor and intermediate grammar mistakes are frequent.
- The actual presentation of the ASC images needs revision as the displayed metrics within the image are not indicative and certain features in the images require expert explanation. Furthermore, fewer-but-larger images would support reader comprehension.
I would appreciate a careful revision of the mentioned points, as well as the marked points in-text.
Masatoshi Yamauchi and Urban Brändstöm
Masatoshi Yamauchi and Urban Brändstöm
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