Auroral alert version 1.0: Two-step automatic detection of sudden auroral intensification from all-sky jpeg images

Yamauchi, Masatoshi; Brändstöm, Urban

doi:https://doi.org/10.5194/egusphere-2022-331

Preprints

https://doi.org/10.5194/egusphere-2022-331

Preprints

17 May 2022

| 17 May 2022

Auroral alert version 1.0: Two-step automatic detection of sudden auroral intensification from all-sky jpeg images

Masatoshi Yamauchi and Urban Brändstöm

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.

Received: 12 May 2022 – Discussion started: 17 May 2022

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Download & links

Preprint (PDF, 4167 KB)

Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Preprint (4167 KB)

Supplement (27 KB)

Download & links

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

18 Apr 2023

Auroral alert version 1.0: two-step automatic detection of sudden aurora intensification from all-sky JPEG images

Masatoshi Yamauchi and Urban Brändström

Geosci. Instrum. Method. Data Syst., 12, 71–90, https://doi.org/10.5194/gi-12-71-2023,https://doi.org/10.5194/gi-12-71-2023, 2023

Short summary

Masatoshi Yamauchi and Urban Brändstöm

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-331', Anonymous Referee #1, 16 Jun 2022

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.

Citation: https://doi.org/10.5194/egusphere-2022-331-RC1
- AC1: 'Reply on RC1 (our plan toward revision)', Masatoshi Yamauchi, 27 Jun 2022
  
  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).
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC1
CC1:
'Comment on egusphere-2022-331', Christian Kehl, 28 Jun 2022
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.
Citation: https://doi.org/10.5194/egusphere-2022-331-CC1
- AC2:
  'Reply on CC1', Masatoshi Yamauchi, 05 Jul 2022
  
  Reply to comments by Christian Kehl (our plan toward revision)
  Thank you for your detailed comments and for reminding that potential readers are much wider than the auroral observation community (main target readers). To cover wider readers, we will add more explanations and figures/table (as written in the reply to reviewer 1), e.g., explaining the aurora image, aurora activity itself, and how to interpret the aurora.
  Also, we will make it clearer that the presented two step method is the first trial of "translation" of how auroral scientists actually judge "onset" of auroral activity in the sky: first evaluate the colour information to judge if it is aurora or not (just using green colour cannot distinguish diffuse aurora or cloud because the morphology is similar to each other, and this is why we need three-four colours), and then evaluate the activity level from both intensity and area within the field-of-view.
  The main user is auroral community scientists and operators (they asked us to describe our method that is already in successful operation) who are familiar with classifying the auroral activity level and ready to apply this method (after modification of the parameters). This is why the old version 0 is already applied in Finland (private communication, 2022).
  In the revision, we would stress that there is no automated identification scheme of "onset" in both machine learning method and expert system method. What so far exist is just "one" category each picture of types of aurora, without telling the activity level, although the activity level is the most important parameter. Thus this is the first trial of such, and evaluation of the method much be done against eye identification method but not machine learning method.
  Even for just classification without intensity, there is no "up to date" automated classification scheme of the aurora better than human eye, which required training of many years. For example, Nanjo et al (2022, Figure 1) and its reference (Clausen & Nickisch, 2018, Figure 1) have three auroral categories "arc" "discrete" "diffuse" as one value for each picture (here "arc" is just only a special form of "discrete" from auroral science viewppoint), but "discrete aurora" and "diffuse aurora" always appears together for all active aurora or during the precursor of active aurora. Also, most of the auroral images are mixed with cloud (it is very rare to have clear sky during night), causing the most updated automated classification end up "ambiguous" (Clausen & Nickisch, 2018) or "Aurora and cloud" (Nanjo et al., 2022). Contrary, our method gives in addition to the percentage of sky coverage for each category, the activity level discrete aurora as L3 parameter.
  Thanks to your comment, we now realised that we have to explain this background in the introduction for wider audience than the auroral community.
  Clausen, L. B. N., & Nickisch, H.: Automatic classificationof auroral images from the OsloAuroral THEMIS (OATH) data set usingmachine learning, J. Geophys. Res., 123, 5640–5647,
  
  https://doi.org/10.1029/2018JA025274, 2018
  Nanjo, S., Satonori Nozawa, S., Yamamoto, M., Kawabata T., Johnsen,M.G., Tsuda, T.T, Hosokawa, K.:
  
  An a auroral detection system using deep learning: real-time operation in Troms{\o{}}, Norway,
  
  https://doi.org/10.21203/rs.3.rs-1090985/v1, 2021.
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC2
  - RC4: 'Reply on AC2', Christian Kehl, 28 Jul 2022
    
    I agree with the porposed changes and I'd appreciate a better introduction to the topic. I also encourage the authors to give that imprived introduction, because readers will have an easier time appreciate and gauge the level of accomplishment if the method,
    
    Citation: https://doi.org/10.5194/egusphere-2022-331-RC4
    
    AC5: 'Reply on RC4', Masatoshi Yamauchi, 29 Jul 2022
    
    Thank you again your encouraging comments. It is also encouraging to know that the topic is relevant to much wider community than auroral community.
    
    Citation: https://doi.org/10.5194/egusphere-2022-331-AC5
RC2:
'Comment on egusphere-2022-331', Christian Kehl, 04 Jul 2022
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.
Citation: https://doi.org/10.5194/egusphere-2022-331-RC2
- AC3: 'Reply on RC2', Masatoshi Yamauchi, 05 Jul 2022
  
  Please see Reply to CC1
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC3
RC3:
'Comment on egusphere-2022-331', Anonymous Referee #3, 24 Jul 2022
The paper presents a fast approach for real-time Aura detection by using RGB images acquired by an All-Sky-Camera (ASC) (i.e., full frame camera with a fish-eye lens, pointing towards the sky), located in an a heated-dome at the Kiruna Observatory. The aim of the authors is to identify intensification of auroral arc with expanding motion, named Local-Arc-Breaking (LAB), and send email alerts when the “Level 6” criterion for LAB aurora detection is satisfied.

The proposed approach is composed of two steps: 1) perform a pixel-wise classification of all the image pixels into different categories (including three aurora categories), based on the color information of the pixel itself; 2) compute a series of indexes based on the percentage of pixels detected for each category and the average luminosity of the most intense aurora pixels. Based on the computed indexes, the alert system can detect most relevant aurora events and trigger an alert.

The topic is interesting and the proposed solution for a real time alert is relevant, especially because it is a fast and not computationally expansive approach (which is crucial for real-time applications). However, there are some critical aspects that should be solved (or at least discussed):

Computation of the thresholds and transferability of the method.

The pixel-wise classification is basically based on thresholding on RGB data, and it relies on a large number of thresholds. This makes the classification fast and computationally easy. However, it requires a proper fine tuning of the threshold levels, that is crucial for an accurate classification. I believe that threshold calibration is mostly based on the authors’ experience, as very little information about this is provided in the paper. The clear consequence of this, is that the procedure may not be transferable to another site, with different environmental conditions and different instruments (as the authors stated in the paper). With this idea in mind, it would be useful if the authors can discuss deeper their choices and provide some more considerations that have led to the derived threshold values, rather than just providing the values themselves.

Camera geometry

Is the camera geometry considered in your study? I understand that you are not trying to reconstruct the location of the aurora (which may be an interesting extension of your study for the future), but the “occupancy” of the pixel classified as aurora (within the different classes), among the total amount of pixels. However, you are using a fish-eye lens (Nikon Nikkor 8 mm), with strong geometrical distortions, that require a proper camera model for correcting them (please, see e.g., https://docs.opencv.org/4.x/db/d58/group__calib3d__fisheye.html). I think that this aspect should be taken into account (e.g., by undistorting the images before any image processing) or at least discussed to see if geometrical distortions have a relevant impact on the accuracy of the classification.

Comparison with different methods

It would be useful to have some comparison with different state-of-the-art approaches (or also widely established methods, if any) to fully grasp the potential of the proposed solution.

Deep Learning

In the paper, the authors state that Neural Networks (NN) are black boxes, difficult to debug, and strongly dependent on the training data. I partially agree with the authors on these statements. However, I believe that Deep Learning (DL) is a powerful tool for identifying the presence of aural events and to classify them, according to a-priori defined classes (as it is done in this paper) and ground truth data (manually classified images, or images classified with the algorithm proposed in this paper). Moreover, if the NN layers are trained with datasets from different locations and cameras, and with proper data augmentation, this may result in a more transferable and generalized approach, that can be applied in different observatories. Additionally, NN is basically based on sequences of filter convolutions. Therefore, it may overcome the limitation of considering each pixel independently from its neighborhood for the classification (of course DL is not the only possible solution for this: spatial filters, Markov Random Fields are few other examples). Eventually, DL may be combined with the step 2 of the proposed solution to build a real time alert framework.

All this thing considered, I understand that the use of DL is out of the scope of this paper (I suggest considering it for future works, though), but I believe that some more references and discussions on recent works that involve DL for aurora image classification may improve the quality and completeness of this work.

Figure and Tables

I believe that figures and tables can be improved. In my opinion, Figure 1 is not clear, and it should be revised (please, see comments in the pdf supplement). Additionally, figures with ASC images are often placed far from the text in which they are cited (even 3 or 4 pages afterwards), and this prevent a smooth read of the paper. I suggest revising figure placement in the text, reducing the number of ASC images to the really informative ones and making them bigger.

Moreover, I believe that the tables containing the thresholding conditions are too verbose and don’t add relevant information in the text. Moreover, they are splitted in different pages, making even harder to read the tables. I suggest summarizing the main concepts of the tables (e.g., the different classes and the main classification criteria) in just one or two tables to be included in the main text of the paper. On the other hand, all the thresholds can be condensed in one table in the appendix.

Background knowledge

As commented by other reviewers, I also believe that some background knowledge should be included in the text to allow a wider community to understand your work.

_______________

Please, refer to the comments included in the pdf supplement for other minor comments.
Citation: https://doi.org/10.5194/egusphere-2022-331-RC3
- AC4: 'Reply on RC3', Masatoshi Yamauchi, 27 Jul 2022
  
  Reply to Reviewer #3 (our plan toward revision)
  Thank you for your encouraging comments and specific suggestions toward improvements including PDF supplement. We plan to revise the paper with stress on following points
  
  #threshold
  
  We now realized that we did not explain the "general guideline" of how to choose each criterion, such as "how many non-auroral light sources we considered", "how can we avoid the overlapping of two criteria", "what is the rough indication of each sub-division in actual aurora (we add figures for this)" etc., such that third person can construct similar criteria for the other site easily.
  #camera geometry
  
  Including camera geometry is a part of future task of considering field-of-view (section 4.5). As explained there, pixels at higher azimuth is less valued than pixels at zenith. We will enhance the explanation and also add explanation why we treat them equality in the method part too.
  #different method
  
  Except Deep Learning, we are not aware of state-of-art method for auroral image analyses. There is a geomagnetic method using geomagnetic deviation only, but success rate is no high for Kiruna all-sky camera.
  #Deep Learning
  
  Thank you for explaining the potential of Deep Learning, and we do actually considering using NN for step 2 (from index values to alert level). We include more explanation on the Deep Learning, and also mention from which part the NN can be combined with our method.
  #Figures
  
  We add 3 or more figures (one or two figures before Figure 1). The placement of the figure and size of figure are LaTex program problem and we make sure they are located correct pages and size before during copy editing.
  #Tables
  
  Placing Tables 1-7 in one place (one page for aurora and one page for non-aurora) such as appendix is a good idea. The Journal does not allow multi-entry table format, and this is why we separated into Tables 1-7. We try if your suggestion is technically possible.
  #Background knowledge
  
  Yes we will add more explanation of background knowledge
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC4

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-331', Anonymous Referee #1, 16 Jun 2022

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.

Citation: https://doi.org/10.5194/egusphere-2022-331-RC1
- AC1: 'Reply on RC1 (our plan toward revision)', Masatoshi Yamauchi, 27 Jun 2022
  
  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).
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC1
CC1:
'Comment on egusphere-2022-331', Christian Kehl, 28 Jun 2022
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.
Citation: https://doi.org/10.5194/egusphere-2022-331-CC1
- AC2:
  'Reply on CC1', Masatoshi Yamauchi, 05 Jul 2022
  
  Reply to comments by Christian Kehl (our plan toward revision)
  Thank you for your detailed comments and for reminding that potential readers are much wider than the auroral observation community (main target readers). To cover wider readers, we will add more explanations and figures/table (as written in the reply to reviewer 1), e.g., explaining the aurora image, aurora activity itself, and how to interpret the aurora.
  Also, we will make it clearer that the presented two step method is the first trial of "translation" of how auroral scientists actually judge "onset" of auroral activity in the sky: first evaluate the colour information to judge if it is aurora or not (just using green colour cannot distinguish diffuse aurora or cloud because the morphology is similar to each other, and this is why we need three-four colours), and then evaluate the activity level from both intensity and area within the field-of-view.
  The main user is auroral community scientists and operators (they asked us to describe our method that is already in successful operation) who are familiar with classifying the auroral activity level and ready to apply this method (after modification of the parameters). This is why the old version 0 is already applied in Finland (private communication, 2022).
  In the revision, we would stress that there is no automated identification scheme of "onset" in both machine learning method and expert system method. What so far exist is just "one" category each picture of types of aurora, without telling the activity level, although the activity level is the most important parameter. Thus this is the first trial of such, and evaluation of the method much be done against eye identification method but not machine learning method.
  Even for just classification without intensity, there is no "up to date" automated classification scheme of the aurora better than human eye, which required training of many years. For example, Nanjo et al (2022, Figure 1) and its reference (Clausen & Nickisch, 2018, Figure 1) have three auroral categories "arc" "discrete" "diffuse" as one value for each picture (here "arc" is just only a special form of "discrete" from auroral science viewppoint), but "discrete aurora" and "diffuse aurora" always appears together for all active aurora or during the precursor of active aurora. Also, most of the auroral images are mixed with cloud (it is very rare to have clear sky during night), causing the most updated automated classification end up "ambiguous" (Clausen & Nickisch, 2018) or "Aurora and cloud" (Nanjo et al., 2022). Contrary, our method gives in addition to the percentage of sky coverage for each category, the activity level discrete aurora as L3 parameter.
  Thanks to your comment, we now realised that we have to explain this background in the introduction for wider audience than the auroral community.
  Clausen, L. B. N., & Nickisch, H.: Automatic classificationof auroral images from the OsloAuroral THEMIS (OATH) data set usingmachine learning, J. Geophys. Res., 123, 5640–5647,
  
  https://doi.org/10.1029/2018JA025274, 2018
  Nanjo, S., Satonori Nozawa, S., Yamamoto, M., Kawabata T., Johnsen,M.G., Tsuda, T.T, Hosokawa, K.:
  
  An a auroral detection system using deep learning: real-time operation in Troms{\o{}}, Norway,
  
  https://doi.org/10.21203/rs.3.rs-1090985/v1, 2021.
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC2
  - RC4: 'Reply on AC2', Christian Kehl, 28 Jul 2022
    
    I agree with the porposed changes and I'd appreciate a better introduction to the topic. I also encourage the authors to give that imprived introduction, because readers will have an easier time appreciate and gauge the level of accomplishment if the method,
    
    Citation: https://doi.org/10.5194/egusphere-2022-331-RC4
    
    AC5: 'Reply on RC4', Masatoshi Yamauchi, 29 Jul 2022
    
    Thank you again your encouraging comments. It is also encouraging to know that the topic is relevant to much wider community than auroral community.
    
    Citation: https://doi.org/10.5194/egusphere-2022-331-AC5
RC2:
'Comment on egusphere-2022-331', Christian Kehl, 04 Jul 2022
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.
Citation: https://doi.org/10.5194/egusphere-2022-331-RC2
- AC3: 'Reply on RC2', Masatoshi Yamauchi, 05 Jul 2022
  
  Please see Reply to CC1
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC3
RC3:
'Comment on egusphere-2022-331', Anonymous Referee #3, 24 Jul 2022
The paper presents a fast approach for real-time Aura detection by using RGB images acquired by an All-Sky-Camera (ASC) (i.e., full frame camera with a fish-eye lens, pointing towards the sky), located in an a heated-dome at the Kiruna Observatory. The aim of the authors is to identify intensification of auroral arc with expanding motion, named Local-Arc-Breaking (LAB), and send email alerts when the “Level 6” criterion for LAB aurora detection is satisfied.

The proposed approach is composed of two steps: 1) perform a pixel-wise classification of all the image pixels into different categories (including three aurora categories), based on the color information of the pixel itself; 2) compute a series of indexes based on the percentage of pixels detected for each category and the average luminosity of the most intense aurora pixels. Based on the computed indexes, the alert system can detect most relevant aurora events and trigger an alert.

The topic is interesting and the proposed solution for a real time alert is relevant, especially because it is a fast and not computationally expansive approach (which is crucial for real-time applications). However, there are some critical aspects that should be solved (or at least discussed):

Computation of the thresholds and transferability of the method.

The pixel-wise classification is basically based on thresholding on RGB data, and it relies on a large number of thresholds. This makes the classification fast and computationally easy. However, it requires a proper fine tuning of the threshold levels, that is crucial for an accurate classification. I believe that threshold calibration is mostly based on the authors’ experience, as very little information about this is provided in the paper. The clear consequence of this, is that the procedure may not be transferable to another site, with different environmental conditions and different instruments (as the authors stated in the paper). With this idea in mind, it would be useful if the authors can discuss deeper their choices and provide some more considerations that have led to the derived threshold values, rather than just providing the values themselves.

Camera geometry

Is the camera geometry considered in your study? I understand that you are not trying to reconstruct the location of the aurora (which may be an interesting extension of your study for the future), but the “occupancy” of the pixel classified as aurora (within the different classes), among the total amount of pixels. However, you are using a fish-eye lens (Nikon Nikkor 8 mm), with strong geometrical distortions, that require a proper camera model for correcting them (please, see e.g., https://docs.opencv.org/4.x/db/d58/group__calib3d__fisheye.html). I think that this aspect should be taken into account (e.g., by undistorting the images before any image processing) or at least discussed to see if geometrical distortions have a relevant impact on the accuracy of the classification.

Comparison with different methods

It would be useful to have some comparison with different state-of-the-art approaches (or also widely established methods, if any) to fully grasp the potential of the proposed solution.

Deep Learning

In the paper, the authors state that Neural Networks (NN) are black boxes, difficult to debug, and strongly dependent on the training data. I partially agree with the authors on these statements. However, I believe that Deep Learning (DL) is a powerful tool for identifying the presence of aural events and to classify them, according to a-priori defined classes (as it is done in this paper) and ground truth data (manually classified images, or images classified with the algorithm proposed in this paper). Moreover, if the NN layers are trained with datasets from different locations and cameras, and with proper data augmentation, this may result in a more transferable and generalized approach, that can be applied in different observatories. Additionally, NN is basically based on sequences of filter convolutions. Therefore, it may overcome the limitation of considering each pixel independently from its neighborhood for the classification (of course DL is not the only possible solution for this: spatial filters, Markov Random Fields are few other examples). Eventually, DL may be combined with the step 2 of the proposed solution to build a real time alert framework.

All this thing considered, I understand that the use of DL is out of the scope of this paper (I suggest considering it for future works, though), but I believe that some more references and discussions on recent works that involve DL for aurora image classification may improve the quality and completeness of this work.

Figure and Tables

I believe that figures and tables can be improved. In my opinion, Figure 1 is not clear, and it should be revised (please, see comments in the pdf supplement). Additionally, figures with ASC images are often placed far from the text in which they are cited (even 3 or 4 pages afterwards), and this prevent a smooth read of the paper. I suggest revising figure placement in the text, reducing the number of ASC images to the really informative ones and making them bigger.

Moreover, I believe that the tables containing the thresholding conditions are too verbose and don’t add relevant information in the text. Moreover, they are splitted in different pages, making even harder to read the tables. I suggest summarizing the main concepts of the tables (e.g., the different classes and the main classification criteria) in just one or two tables to be included in the main text of the paper. On the other hand, all the thresholds can be condensed in one table in the appendix.

Background knowledge

As commented by other reviewers, I also believe that some background knowledge should be included in the text to allow a wider community to understand your work.

_______________

Please, refer to the comments included in the pdf supplement for other minor comments.
Citation: https://doi.org/10.5194/egusphere-2022-331-RC3
- AC4: 'Reply on RC3', Masatoshi Yamauchi, 27 Jul 2022
  
  Reply to Reviewer #3 (our plan toward revision)
  Thank you for your encouraging comments and specific suggestions toward improvements including PDF supplement. We plan to revise the paper with stress on following points
  
  #threshold
  
  We now realized that we did not explain the "general guideline" of how to choose each criterion, such as "how many non-auroral light sources we considered", "how can we avoid the overlapping of two criteria", "what is the rough indication of each sub-division in actual aurora (we add figures for this)" etc., such that third person can construct similar criteria for the other site easily.
  #camera geometry
  
  Including camera geometry is a part of future task of considering field-of-view (section 4.5). As explained there, pixels at higher azimuth is less valued than pixels at zenith. We will enhance the explanation and also add explanation why we treat them equality in the method part too.
  #different method
  
  Except Deep Learning, we are not aware of state-of-art method for auroral image analyses. There is a geomagnetic method using geomagnetic deviation only, but success rate is no high for Kiruna all-sky camera.
  #Deep Learning
  
  Thank you for explaining the potential of Deep Learning, and we do actually considering using NN for step 2 (from index values to alert level). We include more explanation on the Deep Learning, and also mention from which part the NN can be combined with our method.
  #Figures
  
  We add 3 or more figures (one or two figures before Figure 1). The placement of the figure and size of figure are LaTex program problem and we make sure they are located correct pages and size before during copy editing.
  #Tables
  
  Placing Tables 1-7 in one place (one page for aurora and one page for non-aurora) such as appendix is a good idea. The Journal does not allow multi-entry table format, and this is why we separated into Tables 1-7. We try if your suggestion is technically possible.
  #Background knowledge
  
  Yes we will add more explanation of background knowledge
  
  Citation: https://doi.org/10.5194/egusphere-2022-331-AC4

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Masatoshi Yamauchi on behalf of the Authors (07 Oct 2022) Author's response Author's tracked changes Manuscript

EF by Sarah Buchmann (10 Oct 2022) Supplement

ED: Referee Nomination & Report Request started (25 Oct 2022) by Anette Eltner

RR by Anonymous Reviewer #3 (07 Nov 2022)

RR by Anonymous Reviewer #1 (18 Nov 2022)

ED: Reconsider after major revisions (04 Dec 2022) by Anette Eltner

I would like to thank the authors for revising their manuscript by adding many new details and by answering to each of the referee’s comments. I read the manuscript and the comments by and answers to the referees and must however conclude that moderate (and thus major because there is no moderate option) revisions are again necessary before considering the paper for publication. Please note, the manuscript needs to be revised significantly according to the first referee and thus, please, consider each of referee 1’s comment very thoroughly.
Many improvements were already made and issues solved to make the introduced approaches more accessible. Thank you very much for the effort. However, especially the structure of the manuscript is still not satisfactory. The structure is partly chaotic and often too long text segments are present. I know that this might seem contradictory to the first revision, where more explanations were demanded. But the issue is not the explanations themselves but the structure of them. Please, revise the manuscript accordingly and check if subsections are possible. The introduction needs strong revisions because subjects, such as the discussion how machine learning can support the authors system, need to go to the discussion chapter. Also, method descriptions of the workflow need to move to the method chapter and there they should be explained with more structure (again considering subchapters).
It needs to be noted that overall, the manuscript is too long and should be shortened strongly. Please, aim for not more than 25 pages (now there are 35) and thus remove – as suggested by reviewer 1 – segments discussing improvements and adjustment options and maybe leave it for a second paper or consider putting some details to the appendix. There are many repetitions or redundant paragraphs throughout the manuscript, which need to be removed, which also helps shortening the text. There are also quite a few typos and issues regarding the grammar. Please, consider a native speaker to check the entire manuscript before the next submission.
I am looking forward to your revisions because I think that the topic is very interesting and very suitable for GI.

Hide

AR by Masatoshi Yamauchi on behalf of the Authors (09 Feb 2023) Author's response Author's tracked changes Manuscript

EF by Polina Shvedko (10 Feb 2023) Supplement

ED: Publish subject to technical corrections (16 Mar 2023) by Anette Eltner

AR by Masatoshi Yamauchi on behalf of the Authors (22 Mar 2023) Author's response Manuscript

Journal article(s) based on this preprint

18 Apr 2023

Auroral alert version 1.0: two-step automatic detection of sudden aurora intensification from all-sky JPEG images

Masatoshi Yamauchi and Urban Brändström

Geosci. Instrum. Method. Data Syst., 12, 71–90, https://doi.org/10.5194/gi-12-71-2023,https://doi.org/10.5194/gi-12-71-2023, 2023

Short summary

Masatoshi Yamauchi and Urban Brändstöm

Supplement

https://doi.org/10.5194/egusphere-2022-331-supplement

Masatoshi Yamauchi and Urban Brändstöm

Viewed

Total article views: 547 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
390	131	26	547	34	12	10

HTML: 390
PDF: 131
XML: 26
Total: 547
Supplement: 34
BibTeX: 12
EndNote: 10

Views and downloads (calculated since 17 May 2022)

Month	HTML	PDF	XML	Total
May 2022	65	21	6	92
Jun 2022	92	11	0	103
Jul 2022	77	21	15	113
Aug 2022	43	19	0	62
Sep 2022	12	8	0	20
Oct 2022	15	8	1	24
Nov 2022	17	9	0	26
Dec 2022	10	4	0	14
Jan 2023	15	5	0	20
Feb 2023	19	14	0	33
Mar 2023	19	5	2	26
Apr 2023	6	6	2	14
May 2023	0
Jun 2023	0
Jul 2023	0
Aug 2023	0
Sep 2023	0
Oct 2023	0
Nov 2023	0
Dec 2023	0
Jan 2024	0
Feb 2024	0
Mar 2024	0
Apr 2024	0
May 2024	0
Jun 2024	0
Jul 2024	0
Aug 2024	0
Sep 2024	0

Cumulative views and downloads (calculated since 17 May 2022)

Month	HTML	PDF	XML	Total
May 2022	65	21	6	92
Jun 2022	92	11	0	103
Jul 2022	77	21	15	113
Aug 2022	43	19	0	62
Sep 2022	12	8	0	20
Oct 2022	15	8	1	24
Nov 2022	17	9	0	26
Dec 2022	10	4	0	14
Jan 2023	15	5	0	20
Feb 2023	19	14	0	33
Mar 2023	19	5	2	26
Apr 2023	6	6	2	14
May 2023	0
Jun 2023	0
Jul 2023	0
Aug 2023	0
Sep 2023	0
Oct 2023	0
Nov 2023	0
Dec 2023	0
Jan 2024	0
Feb 2024	0
Mar 2024	0
Apr 2024	0
May 2024	0
Jun 2024	0
Jul 2024	0
Aug 2024	0
Sep 2024	0

Viewed (geographical distribution)

Total article views: 465 (including HTML, PDF, and XML) Thereof 465 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 03 Sep 2024

Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (4167 KB)
Metadata XML

Short summary

Potential users of all sky camera images include even power company, tourists and aurora enthusiast. However, these potential users are normally not familiar to interpreting these images. To make them comprehensive for wide users, we developed an automatic evaluation system of the auroral activity level. The method involves two steps: first making a simple set of numbers that describes the auroral activity, and then further simplifying them into several levels (Level 6 is aurora explosion).


Total:	0
HTML:	0
PDF:	0
XML:	0