A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

Round 1

Reviewer 1 Report

In order to realize the extraction of winter wheat, the authors proposed RAunet model of a multi-scale feature deep supervised network with an improved U-Net backbone network incorporating a dual-attention mechanism. The paper has obtained good experimental results, general innovation, and there are the following problems:

1. The analysis and summary of WW extraction need to be further strengthened;

2. Further summarize the main contributions of the paper;

3. The comparison models adopted by the authors are all classical networks, and it is recommended to compare with SOTA model. At the same time, the authors used the improved Unet network for semantic segmentation, and there are a large number of improved U-net networks. It is suggested that the author add the comparison experiments with the existing improved U-net network.

4. Table 1 and Table 2 are the accuracy evaluation results for Figure 11 or for the whole test set?

5. Delete the results of IoU in Table 2;

6. The author did not properly understand the similarities and differences between IoU and mIoU, leading to the misuse of IoU indicators in the experiment and discussion section;

7. Ablation experiment need add in the discussion section to verify the influence of different modules on the results;

8. The conclusion part is too redundant, the focus is not prominent. At the same time, it is necessary to supplement the existing problems of the paper and the next research.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

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Title: A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

Thank you for writing the manuscript titled "A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net" which deals with obtaining spatial distribution information for winter wheat. The aim is to enhance yield estimation. The paper presents an innovative RAunet model, an improved U-Net backbone network with dual-attention mechanisms for extracting winter wheat spatial information from high-resolution images. The manuscript is well written and introduces some novelties on classifier the object using a new branch of neural network. However, some clarifications and improvements can be made to increase the clarity and readable for the reader of Remote Sensing Journals as follows:

1)     Abstract: The technical term in the abstract distracts the readers. I suggest making it simpler and reducing the jargon term. Please modify it to focus on the way improvements of modified classifier.

Rewrite these sentences:

The pyramid input layer consists of four input paths, which are used to fuse feature information at different scales. The encoder of the improved U-Net model in the backbone network is composed of residual block to form feature extraction layer to avoid the gradient explosion caused by the deepening of the network; moreover, an Atrous Spatial Pyramid Pooling (ASPP) structure was introduced to obtain multi-scale WW information. In addition, a Convolutional Block Attention Module (CBAM) dual-attention mechanism was added in the skip connection stage to increase the weight information of WW in both the channel and spatial dimensions, which can strengthen the ability of WW feature extraction. Additionally, the side output layer was set up with multiple classifiers to monitor the output results of each output path at different scales

2)     Introduction

a)      Please reintroduce the WW abbreviation.

b)     Please add some detail in the statistics about the tonnes. And where the WW is major area in China.

c)      Please add some details about the accuracy of this paragraph.

d)     Please explain or introduce the relationship between remote sensing and image segmentation.

e)     This paragraph is too long. Please, divide it into two paragraphs.

 

f)       Please introduce U-Net, Segnet, Resnet, ASPP

3)     Study area and data

a)      Figure 1 is too plain, and please add some explanation of the map. Is it China Province?. What kind of imagery do you use on this image? Please also explain it in the figure caption.

b)     Please add a reference to this sentence:

4)     Methodology

a)      Please Introduce all abbreviations in the body text. Like ASPP

b)     The ground sampling is quite new because both classes (WW and Non-WW) were commonly labelled on the field survey. So please modify Figure 2 to explain the distribution of the field survey and manual points sampling.

c)      The methodology is too complex to understand—so much jargon. Please make a big figure to include the interactions of the jargon.

5)     Experiments and Results

a)      Please change the chapter to Results

b)     Move 4.1, and 4.2 to methodology

c)      The result of the comparison of the models is not stated/ mentioned in the methodology section. Please add the comparison step

d)     The abstract content is misleading since the results and discussion only classify the WW and NWW, not yield estimation.

e)     Explain the yellow lines on this image in Figure 11.

6)     Discussion

a)      Can you explain to me how you got this conclusion (1) For densely distributed small-scale WW patches, the segmentation results obtained were not fine enough, the contours were rough, and there was mutual adhesion between the WW patches, resulting in smaller-scale WW patches that could not be effectively recovered)? I do not see the results.

b)     Please move these paragraphs to the results:

c. Please split this paragraph into two paragraphs.

c)      Conclusion

The conclusion is too long, and please remove the jargon and the point of improvement from this study.

 

 

 

 

Comments for author File: Comments.pdf

The manuscript is well-written, but some abbreviations are not mentioned in the body text.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors made changes according to the comments, but there are still the following problems:

1) Figure 4-8 is not original by the authors and the citation should be added;

2) The abstract of the paper can be appropriately simplified;

3) The text of the paper also contains errors in details that the authors need to check.

Author Response

Please see the attachment

Author Response File: Author Response.docx