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Remote Sensing
Volume 15
Issue 5
10.3390/rs15051384
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Article
Peer-Review Record

A Depth-Wise Separable U-Net Architecture with Multiscale Filters to Detect Sinkholes

Remote Sens. 2023, 15(5), 1384; https://doi.org/10.3390/rs15051384
by Rasha Alshawi 1, Md Tamjidul Hoque 1,* and Maik C. Flanagin 2
Reviewer 1: Anonymous
Reviewer 2:
Shruti Kanga
Reviewer 3:
Chao Chen
Remote Sens. 2023, 15(5), 1384; https://doi.org/10.3390/rs15051384
Submission received: 17 January 2023 / Revised: 18 February 2023 / Accepted: 27 February 2023 / Published: 28 February 2023
(This article belongs to the Special Issue Machine Learning and GeoAI for Remote Sensing Environmental Monitoring)

Round 1

Reviewer 1 Report

I really enjoyed this paper. The authors did a great job of laying out the design of the experiments and explained the results in a concise manner. The Abstract and Summary were both nicely written.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

 

 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

Thanks for the chance to review this MS. The authors explored a depth-wise separable U-Net architecture with multiscale filters to detect sinkholes. In general, the topic is interesting. I would suggest accepting after some revisions. My comments or concerns are summarized as below.

(1) The motivation or purpose should be highlighted in the abstract.

(2) Line 64 on Page 2, the url should be given to instead “here”, that is an unscientific expression.

(3) For 2. Literature Review, the main equation and schematic diagram should be given to better express the principle of U-Net.

(4) For Fig. 13, it should be adjusted, and the text in the figure should be horizontal.

(5) Although the MS is interesting, but the relationship with REMOTE SENSING should be strengthened.

(6) Based on U-Net model, the advanced models have been developed, such as U2-Net. The comparison with U2-Net or other advanced models should be performed.

(7) The following Refs should be read and cited.

Ø  Sun W, Ren K, Meng X, et al. MLR-DBPFN: A Multi-Scale Low Rank Deep Back Projection Fusion Network for Anti-Noise Hyperspectral and Multispectral Image Fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5522914.

Ø  Chen, C., Liang, J., Xie, F., Hu, Z., Sun, W., Yang, G., ... & Zhang, Z. (2022). Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China. International Journal of Applied Earth Observation and Geoinformation, 107, 102711.

Ø   Yang, G., Huang, K., Sun, W., Meng, X., Mao, D., & Ge, Y. (2022). Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 236-254.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

After read the revised manuscript, I think the MS can be accepted.

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