Coastline Recognition Algorithm Based on Multi-Feature Network Fusion of Multi-Spectral Remote Sensing Images
Round 1
Reviewer 1 Report
The authors of the paper used PCA to extract the main components of the shoreline image and to remove of the noise. Next they used the dual attention network and HRnet to extract suspected coastline regions from different angles. In order to extract the coastline, the authors of the paper used the decision set method.
The all main proposed method was well described and illustrated. The paper is well structured, all its elements constitute a coherent logical whole. Nevertheless, the application of the coastline straightening model is little understood. Used these metod is poorly justified and it stands out from coherent content of the paper. In my opinion, this problem of straightening the coastline can be removed without much loss to the whole and quality of these paper. I propose to justify the purposefulness of using this method more, because the simple statement that it serves to display the effect of a solved research problem is not very convincing.
Part 5 of the paper is basically just a conclusions. It lacks general discussion and general description of the results. Part 4 is missing. Part 3.5 is followed by part 5 in the paper. Is the article incomplete? Is this just an authors mistake?
Author Response
Please see the pdf. Thanks.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper is well-written and structured and contains new information for the academic community. The objectives of the work are well highlighted, and the methodological framework and verifications are well documented and acceptable for reading. The discussion and conclusions confirm the proposed approach and model
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Author Response
Please see the pdf.
Author Response File: Author Response.pdf
Reviewer 3 Report
This manuscript introduces the suspicious coastline is extracted from different levels of dual attention network and HRNET network, and a decision set fusion method is proposed to realize coastline extraction, and a coastline straightening model is constructed to visually display and analyze the recognition effect. In summary, the research is interesting and provides valuable results, but the current document has several weaknesses that must be strengthened in order to obtain a documentary result that is equal to the value of the publication.
Strengths:
1. The flow is nice and the paper is easy to follow. The clarity of the writing was appreciated, especially when stating algorithms for comparison.
Weaknesses:
2. In the abstract section, when presenting the main issues of shoreline identification, it is recommended to mark the serial numbers, just like in the summary section.
3. In abstract, how to style may be changed. Declarative sentences are recommended.
4. The first paragraph introducing the research topic may present a much broad and comprehensive view of the problems related to your topic with citations to computer vision authority references (Identification and Detection of Biological Information on Tiny Biological Targets Based on Subtle Differences, Machines 2022, 10(11), 996).
5. Where is part 4?
6. In my naive mind, I don't see from the article by what means this decision set fusion is done.
7. Note that the training of data sets is involved in this article, which should explain the environment and configuration of the computer.
8. When straightening coastlines, is there a basis for straightening the image in a range of 600 pixels adjacent to each other? Or how many pixels will work?
9. Vision technology integrated with deep learning is emerging these years in various engineering fields. The authors may add more state-of-art articles, please refer to Abel orchard based on improved YOLOv4-tiny model and binocular stereo vision; A Study on Long–Close Distance Coordination Control Strategy for Litchi Picking.
Author Response
please see the pdf.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
accept
