Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT
Round 1
Reviewer 1 Report (Previous Reviewer 3)
Dear authors,
The authors have improved the manuscript extensively. Although this has now been significantly improved, discussion from the results and other researches are not clear enough.
Again, I wonder you discuss previous researches about convolutional neural network and computer vision combining your research.
What does it add to ISAR compared with other publications?
What are the advantages of translational motion compensation compared with other publications, are there more application for this approach?
What further distortion controls should be considered (perhaps expectations for future research)?
Best regards
Author Response
Thanks for your comments. Here are our point-to-point answers.
Comment 1: Again, I wonder you discuss previous researches about convolutional neural network and computer vision combining your research.
Answer: In the previous round of reviews, another reviewer made a similar comment. Therefore we have added the relevant content in the introduction of the manuscript.
Comment 2: What does it add to ISAR compared with other publications?
Answer: As we mentioned in the abstract and introduction, the proposed method has better noise robustness compared to conventional methods and is easy to implement. This allows us to compensate for translational motion at low SNR and acquire clear ISAR images of the target.
Comment 3: What are the advantages of translational motion compensation compared with other publications, are there more application for this approach?
Answer: Translational motion compensation is almost essential for ISAR imaging. The proposed method has stronger robustness compared to traditional methods. We believe that the mathematical analysis, experimental results and related literature in the manuscript are sufficient to prove this.
Comment 4: What further distortion controls should be considered (perhaps expectations for future research)?
Answer: In the previous round of reviews, another reviewer made a similar comment. Therefore we have added the expectations for future research in the conclusion of the manuscript.
Reviewer 2 Report (Previous Reviewer 2)
I have no further comments.
Author Response
Thank you again for your review!
Round 2
Reviewer 1 Report (Previous Reviewer 3)
Very unique research done.
Most of my comments have been clarified in this Revision.
I recommend this publication after one minor thing:
Line 240: "Km" vs km" Please check it.
Author Response
We have changed "Km" to "km"
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
I am happy you have explained your method, and have carried out some comparisons with other methods. You present comparative results as images for subjective measures of improvemnt of focus, but also the quantitative Entropy measure, albeit the differences are not large.
Check citation of Table 2
In mathematics, a central dot is not needed to show the product of two variables.
Reviewer 2 Report
See attachment
Comments for author File: 
Comments.pdf
Reviewer 3 Report
Dear authors,
Thank you for sending this paper to the journal Remote sensing. The subject of the study is very interesting and the implementation of a complete protocol that conducts noise-robust translational motion compensation. This reviewer has some comments regarding the clarity of the paper:
Comment 1: Perhaps ‘HOLPT-GSCFT’ is a long abbreviation. To enhance the applicability and readability of abbreviations, please consider correcting it.
Comment 2: Line 28- Perhaps ISAR full name appears for the first time in main body of your article.
Comment 3: Line 59-60- Literature review for the application of third-order polynomials should be mentioned.
Comment 4: Line 71- I wonder a complete paragraph introducing the various steps of signal model (Section 2). Then start the deduced explanation. This comment also applies to Section 3(Line 127).
Comment 5: Line 253-307- A spacing should be included between the number and unit (dB).
Comment 6: Discussion part is insufficient. I wonder you discuss previous researches about convolutional neural network and computer vision combining your research.
Comment 7: Limitation of your research should be mentioned.
Best regards
