the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jul 2023
Improved RepVGG Ground-Based Cloud Image Classification with Attention Convolution
Abstract. Clouds greatly impact the earth's radiation prediction, hydrological cycle, and climate change. Accurate automatic recognition of cloud shape based on ground-based cloud image is helpful to analyze solar irradiance, water vapor content, and atmospheric motion, and then predict photovoltaic power, weather trends, and severe weather changes. However, the appearance of clouds is changeable and diverse, and its classification is still challenging. In recent years, convolution neural network(CNN) has made great achievements in ground-based cloud image classification. However, traditional CNN has a poor ability to associate long-distance clouds, so extracting the global features of cloud images is difficult. Therefore, a ground-based cloud image classification method based on improved convolution neural network RepVGG and attention mechanism is proposed in this paper. Firstly, the proposed method increases the RepVGG residual branch and obtains more local detail features of cloud images through small convolution kernels. Secondly, an improved channel attention module is embedded after the residual branch fusion, which can effectively extract the global features of the cloud images. Finally, the linear classifier is used to classify the ground cloud images. In addition, the warm-up method is introduced to optimize the learning rate in the training stage of the proposed method, and it is lightweight in the inference stage, which can avoid over-fitting and accelerate the convergence speed of the model. The proposed method in this paper is evaluated on MGCD and GRSCD ground-based cloud image datasets, and the experimental results show that the accuracy of this method reaches 98.15 % and 98.07 %, respectively, which exceeds other most advanced methods, and proves that CNN still has room for improvement in ground-based cloud image classification task.
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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.
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Preprint
(2565 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1094', Anonymous Referee #1, 04 Aug 2023
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AC1: 'Reply on RC1', Hongyin Xiang, 29 Aug 2023
We would like to thank the Associate Editor for the precious time and great efforts on reviewing the manuscript. At the same time, we also really appreciate the Anonymous Referees for the objective and pertinent comments, which will help the authors to improve the manuscript. According to the reviewing comments, we have revised the manuscript and our point-by-point responses (in green) to the comments (in black) are given. The modification made in the manuscript is presented in blue.
Citation: https://doi.org/10.5194/egusphere-2023-1094-AC1 -
AC2: 'Reply on RC1', Hongyin Xiang, 29 Aug 2023
We would like to thank the Associate Editor for the precious time and great efforts on reviewing the manuscript. At the same time, we also really appreciate the Anonymous Referees for the objective and pertinent comments, which will help the authors to improve the manuscript. According to the reviewing comments, we have revised the manuscript and our point-by-point responses (in green) to the comments (in black) are given. The modification made in the manuscript is presented in blue.
-
AC1: 'Reply on RC1', Hongyin Xiang, 29 Aug 2023
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RC2: 'Comment on egusphere-2023-1094', Anonymous Referee #2, 21 Oct 2023
This manuscript presents an improved method for automatically classifying all sky images in 7 different categories (6 cloud types plus cloudless conditions). The topic is relevant as enhanced observations of clouds are needed both for meteorological and climate research as well as for photovoltaic power plants management. However, the real advancements presented in this manuscript are relatively marginal (as at the end, the new method improves from 96-97% accuracy to 98% accuracy) and restricted to cloud classification in 7 categories from two pre-defined datasets (so it is not clear if they involve images with other cloud classes, or other conditions such as rain or fog). In addition, no reference is made to cloud cover (or cloud amount) nor to cloud movement, which may be key for PV plant management. Nevertheless, the paper is in general technically sound, and could be suited for publication in Atmospheric Measurement Techniques journal provided that it is previously improved by considering some suggestions and applying some technical corrections.
As an atmospheric scientist, I will focus my review on the introduction, data, and results. Unfortunately, I don’t feel capable of commenting on the methodology itself, as my knowledge of machine learning techniques is very limited.Introduction
L. 33. What do you mean by “its decimeter-level observation”?
L. 35. What do you mean by “its equipment”?
L. 36 and Fig. 1. I would say that this is not necessary. Everyone knows that the view from the above is different of the view from below. The differences in detail and area observed are also quite well known. Moreover, there are other satellites that give much more detail of clouds, despite the image is never as detailed as from the ground.
L. 39. Johnson 1989 is indeed a pioneer work, but it's not about the TSI, but about an original prototype of the “whole sky camera” (WSI). In addition, I would say that Shields is not a coauthor of that report. If you want a reference for the TSI, you can use Long et al 2006: Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images; C. N. Long, J. M. Sabburg, J. Calbó, D. Pagès; Journal of Atmospheric and Oceanic Technology vol. 23, 5(2006) pp: 633-652.
L. 46. Taravat et al 2015 is a too specific reference for a so general statement, which may be found in atmospheric radiaton textbooks of review papers. Moreover “by suppressing short-wave and long-wave solar radiation” is note quite precise. First, other wording (absorbing, scattering,…” could be used; second long-wave solar radiation sound strange, as I think you refer to long-wave (infrared) radiation which is emitted by the ground (and clouds) not to solar radiation as such. Please clarify.
L. 50. I would use “visual” instead of "manual” observations.
L. 51. What do you mean by “low efficiency”?
L. 54. “Home and abroad” sounds strange in a science paper. Use “worldwide” instead.
L. 55-56. This sentence is repetitive.
L. 58. “stratus nimbus” is not a cloud genera. It could be “nimbostratus”
L. 60. There are studies that also used feature extraction before Hu et al 2018.
Figure 2. I would say is the other way around (a/b). For sure, images in (b) are not from Cazorla et al 2008.
L. 93-94. Explain in few words what it is CloudNet, CloudA, AlexNet.
L. 112-114. Please rewrite and clarify. This is the result of the present study? Or is like a summary of the previous paragraph?
L. 127-128. What does “subsoil” cloud image classification mean? Are you anticipating a result of your study in the introduction section?Methods
Figure 3 and Table 1. Why stages go from 1-5 in Table 1 and from 0-4 in fig. 3?
L. 164-168. These sentences are a repetition of introduction.Dataset and experiment
L. 327-328. “each ground-based cloud image sample contains ground-based cloud images taken at the same time” Please rewrite or explain.
Section 3.1.2. Apparently, the text is exactly the same as in section 3.1.1. Please do not repeat and focus on the differences between both datasets. Explain for example if the two datasets contain subsets of images which are the same or if, contrarily, they are totally different. Explain how the “true” classification has been established (visual inspection of images?)
Please do not use “Cs” for clear sky. This is confusing as Cs means “cirrostratus”, which is another cloud genus. For clear sky you may use CS (uppercase) or Cl.
L. 366. “accuracy rate” is repeated.
Is there an index for each genus? What does “n” mean? Should TP, TN, … carry an “i” subindex? Are accuracy, precision,… overall indexes or they correspond to each cloud genus?
L. 370-373. Explain better, and use correct wording (False Positive is repeated). One may think that a sample is either classified in the correct genus or not. So it’s not clear how do you have 4 options.
L- 373. “precision” instead of “accuracy”?
Eq. (19). If Pr and Re are already totals (sums) I don’t understand what are you summing to obtain F1.Results and discussion
Table 4, 5. Why there is a single Accuracy value but values for each cloud genera for the other indexes?
L. 403. The correct classification of the Cu is the largest because in the datasets the number of Cu images is the largest too. I mean that the absolute number is not particularly relevant.
It should be noted that all indices derive from the numbers in the confusion matrices. Therefore, I would present first the matrices, and then the indices, which somewhat summarize what is given in the matrix.
L. 459-461. You don’t need to repeat all numbers that are given in the tables. Eventually, you can highlight some numbers in the discussion.Conclusion
L. 480. You should highlight, at least here (also in the abstract), that the accuracy that you reach is in a classification in 7 classes. There are other papers that use more (and less) cloud categories, so it’s important to make sure that the occasional reader knows to what are you referring to.English and technical corrections.
Acronyms should be defined the first time they appear both in the abstract and in the text.
L. 9. Clouds impact Earth radiation, not only its “prediction”
L. 22. “Accuracy” respect to what? Which is the reference? In other words who or how was the “true” cloud classification established?
L. 27. “covering” instead of “accounting”, I would say.
L. 32. “of the outside to the inside”, it should be “from above”.
L. 45. “budget balance”, I think one of the two words is enough.
L. 70 (and many other places). Do not repeat “Singh et al. (Singh and Glennen, 2005)…”; You can simply write “Singh and Glennen (2005)…”
L. 331. “diagram” is not the adequate word, in my opinion.
L. 414. Is “Ablation” the adequate wording?Citation: https://doi.org/10.5194/egusphere-2023-1094-RC2 -
AC3: 'Reply on RC2', Hongyin Xiang, 06 Nov 2023
We would like to thank the Associate Editor for the precious time and great efforts on reviewing the manuscript. At the same time, we also really appreciate the Anonymous Referees for the objective and pertinent comments, which will help the authors to improve the manuscript. According to the reviewing comments, we have revised the manuscript and our point-by-point responses (in green) to the comments (in black) are given. The modification made in the manuscript is presented in blue.
-
AC3: 'Reply on RC2', Hongyin Xiang, 06 Nov 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1094', Anonymous Referee #1, 04 Aug 2023
-
AC1: 'Reply on RC1', Hongyin Xiang, 29 Aug 2023
We would like to thank the Associate Editor for the precious time and great efforts on reviewing the manuscript. At the same time, we also really appreciate the Anonymous Referees for the objective and pertinent comments, which will help the authors to improve the manuscript. According to the reviewing comments, we have revised the manuscript and our point-by-point responses (in green) to the comments (in black) are given. The modification made in the manuscript is presented in blue.
Citation: https://doi.org/10.5194/egusphere-2023-1094-AC1 -
AC2: 'Reply on RC1', Hongyin Xiang, 29 Aug 2023
We would like to thank the Associate Editor for the precious time and great efforts on reviewing the manuscript. At the same time, we also really appreciate the Anonymous Referees for the objective and pertinent comments, which will help the authors to improve the manuscript. According to the reviewing comments, we have revised the manuscript and our point-by-point responses (in green) to the comments (in black) are given. The modification made in the manuscript is presented in blue.
-
AC1: 'Reply on RC1', Hongyin Xiang, 29 Aug 2023
-
RC2: 'Comment on egusphere-2023-1094', Anonymous Referee #2, 21 Oct 2023
This manuscript presents an improved method for automatically classifying all sky images in 7 different categories (6 cloud types plus cloudless conditions). The topic is relevant as enhanced observations of clouds are needed both for meteorological and climate research as well as for photovoltaic power plants management. However, the real advancements presented in this manuscript are relatively marginal (as at the end, the new method improves from 96-97% accuracy to 98% accuracy) and restricted to cloud classification in 7 categories from two pre-defined datasets (so it is not clear if they involve images with other cloud classes, or other conditions such as rain or fog). In addition, no reference is made to cloud cover (or cloud amount) nor to cloud movement, which may be key for PV plant management. Nevertheless, the paper is in general technically sound, and could be suited for publication in Atmospheric Measurement Techniques journal provided that it is previously improved by considering some suggestions and applying some technical corrections.
As an atmospheric scientist, I will focus my review on the introduction, data, and results. Unfortunately, I don’t feel capable of commenting on the methodology itself, as my knowledge of machine learning techniques is very limited.Introduction
L. 33. What do you mean by “its decimeter-level observation”?
L. 35. What do you mean by “its equipment”?
L. 36 and Fig. 1. I would say that this is not necessary. Everyone knows that the view from the above is different of the view from below. The differences in detail and area observed are also quite well known. Moreover, there are other satellites that give much more detail of clouds, despite the image is never as detailed as from the ground.
L. 39. Johnson 1989 is indeed a pioneer work, but it's not about the TSI, but about an original prototype of the “whole sky camera” (WSI). In addition, I would say that Shields is not a coauthor of that report. If you want a reference for the TSI, you can use Long et al 2006: Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images; C. N. Long, J. M. Sabburg, J. Calbó, D. Pagès; Journal of Atmospheric and Oceanic Technology vol. 23, 5(2006) pp: 633-652.
L. 46. Taravat et al 2015 is a too specific reference for a so general statement, which may be found in atmospheric radiaton textbooks of review papers. Moreover “by suppressing short-wave and long-wave solar radiation” is note quite precise. First, other wording (absorbing, scattering,…” could be used; second long-wave solar radiation sound strange, as I think you refer to long-wave (infrared) radiation which is emitted by the ground (and clouds) not to solar radiation as such. Please clarify.
L. 50. I would use “visual” instead of "manual” observations.
L. 51. What do you mean by “low efficiency”?
L. 54. “Home and abroad” sounds strange in a science paper. Use “worldwide” instead.
L. 55-56. This sentence is repetitive.
L. 58. “stratus nimbus” is not a cloud genera. It could be “nimbostratus”
L. 60. There are studies that also used feature extraction before Hu et al 2018.
Figure 2. I would say is the other way around (a/b). For sure, images in (b) are not from Cazorla et al 2008.
L. 93-94. Explain in few words what it is CloudNet, CloudA, AlexNet.
L. 112-114. Please rewrite and clarify. This is the result of the present study? Or is like a summary of the previous paragraph?
L. 127-128. What does “subsoil” cloud image classification mean? Are you anticipating a result of your study in the introduction section?Methods
Figure 3 and Table 1. Why stages go from 1-5 in Table 1 and from 0-4 in fig. 3?
L. 164-168. These sentences are a repetition of introduction.Dataset and experiment
L. 327-328. “each ground-based cloud image sample contains ground-based cloud images taken at the same time” Please rewrite or explain.
Section 3.1.2. Apparently, the text is exactly the same as in section 3.1.1. Please do not repeat and focus on the differences between both datasets. Explain for example if the two datasets contain subsets of images which are the same or if, contrarily, they are totally different. Explain how the “true” classification has been established (visual inspection of images?)
Please do not use “Cs” for clear sky. This is confusing as Cs means “cirrostratus”, which is another cloud genus. For clear sky you may use CS (uppercase) or Cl.
L. 366. “accuracy rate” is repeated.
Is there an index for each genus? What does “n” mean? Should TP, TN, … carry an “i” subindex? Are accuracy, precision,… overall indexes or they correspond to each cloud genus?
L. 370-373. Explain better, and use correct wording (False Positive is repeated). One may think that a sample is either classified in the correct genus or not. So it’s not clear how do you have 4 options.
L- 373. “precision” instead of “accuracy”?
Eq. (19). If Pr and Re are already totals (sums) I don’t understand what are you summing to obtain F1.Results and discussion
Table 4, 5. Why there is a single Accuracy value but values for each cloud genera for the other indexes?
L. 403. The correct classification of the Cu is the largest because in the datasets the number of Cu images is the largest too. I mean that the absolute number is not particularly relevant.
It should be noted that all indices derive from the numbers in the confusion matrices. Therefore, I would present first the matrices, and then the indices, which somewhat summarize what is given in the matrix.
L. 459-461. You don’t need to repeat all numbers that are given in the tables. Eventually, you can highlight some numbers in the discussion.Conclusion
L. 480. You should highlight, at least here (also in the abstract), that the accuracy that you reach is in a classification in 7 classes. There are other papers that use more (and less) cloud categories, so it’s important to make sure that the occasional reader knows to what are you referring to.English and technical corrections.
Acronyms should be defined the first time they appear both in the abstract and in the text.
L. 9. Clouds impact Earth radiation, not only its “prediction”
L. 22. “Accuracy” respect to what? Which is the reference? In other words who or how was the “true” cloud classification established?
L. 27. “covering” instead of “accounting”, I would say.
L. 32. “of the outside to the inside”, it should be “from above”.
L. 45. “budget balance”, I think one of the two words is enough.
L. 70 (and many other places). Do not repeat “Singh et al. (Singh and Glennen, 2005)…”; You can simply write “Singh and Glennen (2005)…”
L. 331. “diagram” is not the adequate word, in my opinion.
L. 414. Is “Ablation” the adequate wording?Citation: https://doi.org/10.5194/egusphere-2023-1094-RC2 -
AC3: 'Reply on RC2', Hongyin Xiang, 06 Nov 2023
We would like to thank the Associate Editor for the precious time and great efforts on reviewing the manuscript. At the same time, we also really appreciate the Anonymous Referees for the objective and pertinent comments, which will help the authors to improve the manuscript. According to the reviewing comments, we have revised the manuscript and our point-by-point responses (in green) to the comments (in black) are given. The modification made in the manuscript is presented in blue.
-
AC3: 'Reply on RC2', Hongyin Xiang, 06 Nov 2023
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Chaojun Shi
Leile Han
Ke Zhang
Hongyin Xiang
Xingkuan Li
Zibo Su
Xian Zheng
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2565 KB) - Metadata XML