A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
, Lei Huang 1,2, Ying Zhang 1,2, Haitian Zhu 1,2 and Xiaotong Yang 3Round 1
Reviewer 1 Report (Previous Reviewer 1)
See attached file
Comments for author File: 
Comments.pdf
Author Response
(1) Please further explain the correspondence between [] and [] in equation (7).
Re: The corresponding contents are redescribed in the original text.
Some explanation:
Assuming that H(DN) is the value after the histogram equalization, the H(DN) value of the original image and the reference image is a many-to-many mapping relationship based on the minimum criterion of . Here is an example, the original image DN values 1-10 (Hr (DN) is 1) are mapped to the reference image DN values 1-6 (Hr (DN) is 1). The DN values 1-10 are equivalent to in the article, and the DN values 1-6 are equivalent to .
Reference image Hr(DN)
0 1 1 1 1 1 1 2 3 6 6 9 12 15 19 25 25 32 38 44 49 49 54 59 62 65 68 68 70 72 74 76 76 78 79 80 81 82 82 84 84 85 86 86 87 88 88 89 89 89 90 90 91 91 91 92 92 93 93 93 94 94 94 95 95 95 95 96 96 96 96 97 97 97 97 98 98 98 98 98 98 98 99 99 99 99 99 99 99 100 100 100 100 100 100 100 100 101 101 101 101 101 101 101 101 101 102 102 102 102 102 102 102 102 102 102 103 103 103 103 103 103 103 103 103 103 103 104 104 104 104 104 104 104 104 104 104 104 105 105 105 105 105 105 105 105 105 105 105 105 106 106 106 106 106 106 106 106 106 106 106 107 107 107 107 107 107 107 107 107 107 108 108 108 108 108 108 108 108 108 108 109 109 109 109 109 109 109 110 110 110 110 110 110 111 111 111 111 111 112 112 112 112 112 113 113 113 114 114 114 115 115 115 116 117 118 119 119 120 121 123 127 132 132 142 160 184 207 207 223 233 239 243 246 246 248 250 251 251 251 252 253 253 253 254 254 254 254 254 254 254 254 254 254 254 255
The orginal image Ho(DN)
0 1 1 1 1 1 1 1 1 1 1 3 4 7 10 13 15 18 21 22 23 23 24 24 25 25 26 26 27 27 28 28 28 29 29 29 30 30 30 31 31 31 31 32 32 32 32 33 33 33 33 34 34 34 34 35 35 35 35 36 36 36 36 37 37 37 37 38 38 38 38 38 39 39 39 39 40 40 40 40 40 41 41 41 41 41 42 42 42 42 43 43 43 43 43 44 44 44 44 44 45 45 45 45 46 46 46 46 46 47 47 47 47 48 48 48 48 49 49 49 49 50 50 50 51 51 51 51 52 52 52 53 53 53 54 54 54 55 55 55 56 56 57 57 57 58 58 59 59 60 60 61 62 62 63 64 64 65 66 67 68 70 71 73 75 77 79 82 86 90 98 121 166 199 214 224 229 233 236 238 240 242 243 244 245 245 246 247 247 248 248 249 249 249 250 250 250 250 250 251 251 251 251 251 251 251 252 252 252 252 252 252 252 252 252 253 253 253 253 253 253 253 253 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 254 255 255 255
(2) How was c in Figure 4 obtained? What kind of processing was done to what kind of image in
the process of obtaining c?
Re: It is redescribed in Section 3.1
“Figure 4c shows the land‒sea reference mask, which is obtained by setting a threshold T to distinguish the light and dark objects in Figure 4a.”
Author Response File: 
Author Response.docx
Reviewer 2 Report (Previous Reviewer 4)
From my observation, the manuscript has shown improvement based on my previous comments. However, I still have some revisions that should be considered before it can be accepted for publication in this journal:
In lines 32-36 of page 1, the authors should cite other recent papers on Relative Radiometric Normalization that support the statement made. Please include references to the following sources:
[x] https://doi.org/10.1080/01431161.2022.2102951
[xx] https://doi.org/10.1109/LGRS.2017.2743209
2- What is meant by "relative radiation normalization"? Clarification is needed. please use relative radiometric normalization (RRN) and please define this based the mentined papers in the comments 1.
3- Throughout the paper, the term "radiation" is frequently used. Is this referring to surface reflectance? It is important to differentiate between "Top of Atmosphere" and "surface reflectance." If the authors are discussing surface reflectance, it should be noted that the images have already undergone radiometric correction using absolute methods. If this is not the case, the term "Top of Atmosphere" could be more appropriate, as it is affected by atmospheric conditions.
4- On page 3, lines 133-134, there is a well-constructed sentence that I find commendable. Please provide a citation for the phrase "Different from the method which fits the features of overlapping region images as previously mentioned." It would be beneficial to reference the methods being discussed [x,xx].
5- Please add the computetion times for the proposed method and the considered method for comparison. I think HM method is faster, but it is not enoghe most of the time for the color consistancy.
I kindly request that you proofread the manuscript and replace uncommon technical terms with more commonly used ones. For instance, "dense relative radiometric normalization" can be used instead of "distribution-based relative radiometric normalization." Furthermore, I suggest reconsidering the use of "radiation," which is commonly associated with thermal images. If the authors are referring to corrections of DN values or surface reflectance, please utilize these terms for improved comprehension.
Author Response
1- In lines 32-36 of page 1, the authors should cite other recent papers on Relative Radiometric Normalization that support the statement made. Please include references to the following sources:
[x] https://doi.org/10.1080/01431161.2022.2102951
[xx] https://doi.org/10.1109/LGRS.2017.2743209
Re: The relevant content has been rewritten, and the above references [x] [xx] have also been cited.
‘Relative radiometric normalization (RRN) can be defined as the procedure of adjusting radiometric (or color) distortions from grey levels of a multispectral subject image based on a multispectral reference image[1]. Since multiple images in a mosaic dataset may be captured by multiple sensors, under different illumination conditions or from different seasons, the color statistics inside a single image or between images are inconsistent, which restricts their usage [2]. Therefore, it is necessary to perform RRN processing before image mosaicking.’
2- What is meant by "relative radiation normalization"? Clarification is needed. please use relative radiometric normalization (RRN) and please define this based the mentioned papers in the comments 1.
Re: Revised in the first comment.
3- Throughout the paper, the term "radiation" is frequently used. Is this referring to surface reflectance? It is important to differentiate between "Top of Atmosphere" and "surface reflectance." If the authors are discussing surface reflectance, it should be noted that the images have already undergone radiometric correction using absolute methods. If this is not the case, the term "Top of Atmosphere" could be more appropriate, as it is affected by atmospheric conditions.
Re: The relevant content has been in Section 1.(Line 134, Line 146-150, )
4- On page 3, lines 133-134, there is a well-constructed sentence that I find commendable. Please provide a citation for the phrase "Different from the method which fits the features of overlapping region images as previously mentioned." It would be beneficial to reference the methods being discussed [x,xx].
Re: adjusted.
“Different from the method which fits the pixel-to-pixel features of overlapping region images as previously mentioned, histogram matching is a distribution−based color consistency matching method [19,41]. “
- Please add the computationtimes for the proposed method and the considered method for comparison. I think HM method is faster, but it is not enghe most of the time for the color consistency.
Re: The calculation time has been added to Section 3.2
Comments on the Quality of English Language
I kindly request that you proofread the manuscript and replace uncommon technical terms with more commonly used ones. For instance, "dense relative radiometric normalization" can be used instead of "distribution-based relative radiometric normalization." Furthermore, I suggest reconsidering the use of "radiation," which is commonly associated with thermal images. If the authors are referring to corrections of DN values or surface reflectance, please utilize these terms for improved comprehension.
Re: The sentence "distribution-based" is quoted from “Radiometric Normalization Using a Pseudo−Invariant Polygon Features−Based Algorithm with Contemporaneous Sentinel−2A and Landsat−8 OLI Imagery”,
Author Response File: Author Response.docx
Reviewer 3 Report (Previous Reviewer 2)
In this study, authors have proposed a color-matching method for mosaicking of HY-1 satellite images of the Antarctica region.
Comments/Suggestions
· It is suggested to make a table of satellite specifications, instead of text.
· It is suggested to make a table of specifications of data used.
· Relevant feature-based image stitching papers are missing in the literature. Some are as follows:
o Forero, M. G., Mambuscay, C. L., Monroy, M. F., Miranda, S. L., Méndez, D., Valencia, M. O., & Gomez Selvaraj, M. (2021). Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops. Plants, 10(9), 1791. https://doi.org/10.3390/plants10091791
o Sharma, S. K., Jain, K., & Shukla, A. K. (2023). A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching. Applied Sciences, 13(10), 6015. https://doi.org/10.3390/app13106015
o Mukherjee, D.; Jonathan Wu, Q.M.; Wang, G. A comparative experimental study of image feature detectors and descriptors. Mach. Vis. Appl. 2015, 26, 443–466.
o M. Diarra, P. Gouton and A. K. Jérôme, "A Comparative Study of Descriptors and Detectors in Multispectral Face Recognition," 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, Italy, 2016, pp. 209-214, doi: 10.1109/SITIS.2016.41.
· Figure citations should be in sequence in the text.
· Conclusions can be improved.
Grammatical mistakes were detected in some places in the manuscript.
Author Response
It is suggested to make a table of satellite specifications, instead of text.
Re: Table 1 has been added for related explanations.
It is suggested to make a table of specifications of data used.
Re: Table 2 has been added for related explanations.
Relevant feature-based image stitching papers are missing in the literature. Some are as follows:
o Forero, M. G., Mambuscay, C. L., Monroy, M. F., Miranda, S. L., Méndez, D., Valencia, M. O., & Gomez Selvaraj, M. (2021). Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops. Plants, 10(9), 1791. https://doi.org/10.3390/plants10091791
o Sharma, S. K., Jain, K., & Shukla, A. K. (2023). A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching. Applied Sciences, 13(10), 6015. https://doi.org/10.3390/app13106015
o Mukherjee, D.; Jonathan Wu, Q.M.; Wang, G. A comparative experimental study of image feature detectors and descriptors. Mach. Vis. Appl. 2015, 26, 443–466.
o M. Diarra, P. Gouton and A. K. Jérôme, "A Comparative Study of Descriptors and Detectors in Multispectral Face Recognition," 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, Italy, 2016, pp. 209-214, doi: 10.1109/SITIS.2016.41.
Re: The relevant content has been added into the section “Introduction” as below:.
“Image mosaicking is a technique to seamlessly fuse multiple images with overlapping areas into a single composite image [2]. Image resgistraion is an underlying requirement for a successful mosaic. On the one hand, for images without geographic reference information, the purpose of image registration and stitching can be achieved by finding the feature information on the image [16,17], and then establishing a model based on the feature information [18]. On the other hand, for images with geographic reference information, image registration can be directly based on geographic information. In general, geographic registration is mostly used for satellite imagery.“
Figure citations should be in sequence in the text.
Re: The text has been adjusted according to your comments.
Conclusions can be improved.
Re: The conclusion has been expanded as below:
“In this paper, images from China's Haiyang-1 satellite coastal zone imager (CZI) were used to study the color uniformity of mosaic images in Antarctica based on MASK illumination simulation theory and image color matching via histogram specification matching. For a single image, a method based on MASK theory combined with an auxil-iary mask was used, and the uneven illumination effect of the image was effectively eliminated. In the process of multiple image color matching, a segmental color-matching method based on the threshold value is proposed for the Antarctic region, compared with the results of the traditional histogram matching method and the wallis method, the results obtained by the traditional histogram method and the method in this paper are closest to the histogram of the reference image, but the color distortion of the traditional histogram method is serious, and the results of the wallis method show that the gray value distribution of the glacier target is lower than that of the reference image. The method in this paper is the best at eliminating the color difference between the images and the overall visual effect. Quantitative evaluation shows that the proposed method achieves good results in eliminating color difference in multiple images, while achieving a good balance between increasing image detail and improving visual effects.”
Comments on the Quality of English Language
Grammatical mistakes were detected in some places in the manuscript.
Re: Modified
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report (Previous Reviewer 4)
The authors well addressed my concerns.
Reviewer 3 Report (Previous Reviewer 2)
All the comments/suggestions have been addressed by the authors.
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
See the attached file.
Comments for author File: Comments.pdf
Reviewer 2 Report
In this study authors have proposed a color matching method for mosaicking of HY-1 satellite images of Antartica region.
Comments/Suggestions
· Introduction section line number 29-30, rewrite the sentence.
· Introduction section line number 41-44, it is suggested to make a table of satellite specifications, instead of text.
· Relevant feature based image stitching papers are missing in the literature. Some are as follows:
o Forero, M. G., Mambuscay, C. L., Monroy, M. F., Miranda, S. L., Méndez, D., Valencia, M. O., & Gomez Selvaraj, M. (2021). Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops. Plants, 10(9), 1791. https://doi.org/10.3390/plants10091791
o Sharma, S. K., Jain, K., & Shukla, A. K. (2023). A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching. Applied Sciences, 13(10), 6015. https://doi.org/10.3390/app13106015
o Mukherjee, D.; Jonathan Wu, Q.M.; Wang, G. A comparative experimental study of image feature detectors and descriptors. Mach. Vis. Appl. 2015, 26, 443–466.
o M. Diarra, P. Gouton and A. K. Jérôme, "A Comparative Study of Descriptors and Detectors in Multispectral Face Recognition," 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, Italy, 2016, pp. 209-214, doi: 10.1109/SITIS.2016.41.
· Figure citations should be in sequence in the text (line number 125).
· Figure 2 is not cited in the text.
Grammatical errors are detected at some places in the manuscript.
Reviewer 3 Report
1、In paragraph 3, the application of satellite-based data products is supplemented with some recent references.
2、Line 65-66, reference 2 is more closely related to paragraphs 4 and 5.
3、Line 134-135, why choose cumulative histogram for a better visual effect?
4、Line 217, In Table 1, why is the statistical difference between Image 2 and Image 3 larger than that in Image 1?
5、The resolution of Figures 5, 6, and 9 should be improved.
6、Please check for grammatical bugs and typos.
Please check for grammatical bugs and typos.
Reviewer 4 Report
The authors have presented a method for color consistency matching in mosaic HY-1 satellite images. While the manuscript has addressed small aspects, there are still some major concerns that need to be addressed to enhance the quality of the paper. Please consider the following comments:
1- The introduction needs improvement. In the first paragraph, please provide citations to support the explanation of radiometric distortions that significantly impact satellite imagery. Please refer to the following papers:
[1] P. M. Teillet, "Image correction for radiometric effects in remote sensing," Int. J. Remote Sens., vol. 7, no. 12, pp. 1637-1651, Dec. 1986.
2- Color consistency matching for mosaicking has been extensively studied. Please include recent papers that discuss Relative Radiometric Normalization (RRN), which is the first step in mosaicking. Some suggested references are:
[1] https://doi.org/10.1016/j.isprsjprs.2017.11.012
[2] https://doi.org/10.1080/01431161.2022.2102951
[3] https://doi.org/10.1109/LGRS.2017.2743209
[4] https://doi.org/10.1016/j.isprsjprs.2020.10.006
3- The review paper by X. Li, et al., titled "Remote sensing image mosaicking: Achievements and challenges," should be included in the introduction. It provides valuable insights that can enhance the introduction section.
(X. Li, et al., "Remote sensing image mosaicking: Achievements and challenges," IEEE Geosci. Remote Sens. Mag., vol. 7, no. 4, pp. 8-22, Dec. 2019).
4- The motivation for developing the proposed method is currently stated as "It is often difficult to acquire a good result using color-matching methods commonly used in remote sensing software." It would be beneficial to provide a more in-depth reason for conducting this study. Please explain the specific problem that has not been adequately addressed by previous researchers.
5- The authors have presented a gray-level segmentation color-matching method to address the issue of image overstretch in the Antarctic image color-matching process. Please bring reference [x] and discuss the differences between the proposed method and the approach described in the reference.
[x] https://doi.org/10.3390/app13042525
6- In the last paragraph of the introduction, please highlight the novelties of the proposed method. Additionally, mention that the Dark set–Bright set (DB) technique, which was introduced in the following references, is utilized for color consistency:
Hall, F. G., et al., "Radiometric rectification: Toward a common radiometric response among multidate, multisensor images," Remote Sens. Environ., vol. 35, no. 1, pp. 11-27, 1991.
P. S. Chavez, "An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data," Remote Sens. Environ., vol. 24, no. 3, pp. 459-479, Apr. 1988.
7- The methodology section needs improvement. Begin with a flowchart at the start of the section to provide an overview of the images and the steps involved in the proposed method. Then, provide a more detailed explanation of each step. It should be noted that the authors assume the images have 8-bit information, which may not be suitable for 16-bit images unless they are rescaled to 8-bit. Please address this issue.
8- Consider including the computation time required for the proposed method.
11- The proposed method should be compared with IRMAD, Histogram Matching, and Simple Regression methods in terms of RMSE and other relevant metrics.
[IRMAD] M. J. Canty and A. A. Nielsen, "Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation," Remote Sens. Environ., vol. 112, no. 3, pp. 1025-1036, Mar. 2008.
