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  • Zhihui Yuan1,2,3,
  • Tianjiao Chen1,2 and
  • Xuemin Xing2,4,*
  • et al.

Reviewer 1: Anonymous Reviewer 2: Anonymous Reviewer 3: Anonymous Reviewer 4: Anonymous

Round 1

Reviewer 1 Report

The manuscript extends BM3D, a most effective filtering algorithm for image denoising, to the cluster-analysis (CA)-based multi-baseline phase unwrapping (MBPU) method to improve the clustering accuracy and robustness of the MBPU method. This study is meaningful and valuable. However, some concerns and suggestions here:

  1. The contributions of this study are not clearly stated in the Abstract. Please add a more detailed description.
  2. The authors simply apply the existing BM3D SAR speckle filter to reduce the speckle noise to the multibaseline phase unwrapping, so as to improve the clustering accuracy and robustness of the CA algorithm. However, the problems of triditional BM3D de-speckling algorithm for MBPU should be discovered, and the authors should make some improvement to BM3D so as to boost the innovation.
  3. It is better to quote the relevant references corresponding to (1).
  4. Page 4, line 162-165, when introducing BM3D algorithm, it is best to refer to the corresponding references.
  5. It is suggested to add four virtual line boxes to each graph in Figure 3, which represent four main parts of Data preprocessing,BM3D denoising, Denormalization and Post-processing respectively, to facilitate understanding and comparison.
  6. It is suggested to add a description after Section 4.2 and before Section 4.2.1 according to Figure 3 to outline the five different filtering strategies. In addition, Section 4 is a little simple overall. It is suggested to write it in more detail.
  7. Page 11, the data in Table 1, 2 and 3 are incomplete. The experimental results without filtering and all five different filtering strategies should be given.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

  • Compared with the traditional CA-based MBPU method, the advantage after adding BM3D is that it can greatly improve the 22 clustering accuracy and robustness of the CA algorithm………how did u check this accuracy
  • Line 106 to111, give reference for this problem.
  • Figure 1: scale-bar, what it represents, what is the unit. 1 to 7 has no meaning unless u say what is the unit, n what it represents.
  • Figure 3: does the blue, brown n white colour represent anything. If yes, explain in the footnote.
  • Equations 5 to 8. Provide references for the equation, if these are not your equations.
  • The conclusion is very weak. Improve by stating your key findings. Especially focus on the parameters which highlight the accuracy of your method.
  • Lines 29 to 31….Interferometric Synthetic Aperture Radar (InSAR) can acquire multiple synthetic radar images, measure the interferometric phase, and finally obtain the digital elevation model (DEM) of the observed region [1-2]…. Give proper citations, for example, work done by A. Ferretti, or work done some latest publications…like you may see (https://doi.org/10.3390/rs13234741)
  • The BM3D method is already introduced in the literature (your ref 28-29). So clearly highlight your novelty and addition to the existing method.
  • Your introduction, basic principle of CA-based MBPU method, and BM3D algorithm have very high plagiarism. This cannot be accepted for a journal like RS. Please rewrite these sections, ensuring there is no plag.
  • More than 6% plagiarism is from below, kindly avoid the plagiarism:

Yuan, Z., Lu, Z., Chen, L., & Xing, X. (2020). A closed-form robust cluster-analysis-based multibaseline InSAR phase unwrapping and filtering algorithm with optimal baseline combination analysis. IEEE Transactions on Geoscience and Remote Sensing, 58(6), 4251-4262.

Author Response

Please see the attachment.

Author Response File: Author Response.doc

Reviewer 3 Report

The article presents a new approach to processing radar images in the phase unwrapping process. The proposed solution concerns the use of Block-matching and 3D (BM3D) alghoritm to improve filtering for image denoising in widely used cluster analysis based multibaseline phase uwrapping (CA-MBPU) method. The development of this methods used was discussed. The analysis of the CA-MBPU strengths and weaknesses prompted the authors to implement BM3D methods in order to improve the phase unwrapping accuracy and the robustness of the CA-based MBPU method. The authors tested the proposed solution on theoretical and practical examples. The obtained results indicate an improvement in the phase unwrapping accuracy and the robustness of the CA-based MBPU method. 

Author Response

Thank you for your positive comments on this manuscript.

Reviewer 4 Report

Original Submission

Recommendation

Minor revision

Comments to Author:

 

Title:  

 

 

 BM3D Denoising for Cluster-Analysis-Based Multibaseline InSAR Phase-Unwrapping Method

 

Overview and general recommendation.

This paper is demonstrating usage of SAR methodology and some important applications. This technique is very important and somehow accurate and has lots of important applications. First of all, as a person with more than 20 years familiarity with SAR/InSAR/RADAR data, I like this paper very much; but as a scientist, I have to say the truth about the material and to be honest.

The Abstract is OK: summarizing the idea and concepts inside the paper is OK, but I feel you need to write it again; English is a bit problem!! I think it is better/must to give it to a native person to review (it is a big must). I feel also like the abstract is raw: pls think more about what you have done in the paper and do the abstract again (it is a must).

Introduction is very good. But, I think in some positions, some important corrections must be done; but, overall merit is good.

Pls improve the quality of Figs> they are very bad; unacceptable.

Results, outlook, are needed to be reconsider seriously; but overall merit is OK.

Conclusion is very bad! write it again pls.

Q: did you compare the result with other PU like SNAPU? Kalman Filter etc?

I like this paper very much: good experiments have been done; however, I think this work must be improved and lots of things to do; agreed? I think the paper is very raw, and must be improved tremendously; but overall merit is acceptable.

 

Detailed comments:

 

Fig1. Put xtitle and y one pls è Improve the qualities of all Figs! You have 2 Fig1?

Line 201-210 messy texts.

Fig6 and 7 are inside each other è fix this.

Table 4? What is this?

 

 

 

 

 

 

 

 

 

 

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.doc

Round 2

Reviewer 1 Report

The revised version has been improved, however, the introduction is not enough, it can be improved to better present the research status. Moreover, the improvement if this paper on BM3D de-noising method is still not clear presented. So I recommend major revision.

Author Response

Thank you very much for your valuable comments. According to your suggestion, we have improved the introduction and added some relevant references. Please see the blue font in the revised version.

In addition, we have rewritten the headline of section 3 and added two secondary headings to describe the improvement of BM3D denoising method clearly. Please see the blue font in the revised version.

Author Response File: Author Response.docx