A Remote Sensing Image Fusion Method Combining Low-Level Visual Features and Parameter-Adaptive Dual-Channel Pulse-Coupled Neural Network

Round 1

Reviewer 1 Report

Detailed Comments:

1. The summary of the related works maybe not sufficient. Some variational optimization based methods are needed.

2. The motivation and contribution of the proposed method should be further summarized.

3. The size of symbols should be designed clearly.

4. The commonly used evaluation metrics, such as Q4, SAM and SCC, should be used in the experiments. Besides, the definitions of the metrics such as IE and MI used in Section 4 should be clarified.

5. The group of the comparison methods should be further clarified.

6. In the experiments, it is also necessary to compare with other state-of-the-art methods, such as some variational optimization based pansharpening methods.

 

7. In Section 4.1, since the pansharpening results between the proposed and the comparison methods are so similar from Fig.5 to Fig.8, error images of the results in MSE or SAM metric seem necessary to discern the differences between different methods visually.

Author Response

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Reviewer 2 Report

1. Authors have written/mentioned more about the generic information and they have focused more on their contribution in the paper.

2. Introduction and literature survey section are short hence must be written more elaborately.

3. References should correctly be cited. The latest references should be cited.

4. If the images are speckled, what will be the effect of proposed work's result. Remark this and show a small analysis in result section. Some related references are given below:

-Review on nontraditional perspectives of synthetic aperture radar image despeckling

-A Review on SAR Image and its Despeckling

- MSPB: intelligent SAR despeckling using wavelet thresholding and bilateral filter for big visual radar data restoration and provisioning quality of experience in real-time remote sensing

-A new SAR image despeckling using correlation based fusion and method noise thresholding

 

 

Author Response

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Author Response File: Author Response.docx